# pos

id: 10_1101-2021_02_13_429885
pos: NNS
token:
  https://www.codecogs.com/eqnedit.php?latex=v%20%3D%20%5Cdfrac%7B%5Cpi%7D%7B2(1-%5Cpi)%20%2B%20%5Cpi%20p%7D#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=v%20%3D%20%5Cdfrac%7B%5Cpi%7D%7B2(1-%5Cpi)%20%2B%20%5Cpi%20p%7D#0


id: 10_1101-2021_02_13_429885
pos: NN
token:
  https://www.codecogs.com/eqnedit.php?latex=c%20%3D%20%5Cdfrac%7Bv%5B(p-2)%5Cpi%20%2B%202%5D%7D%7Bm%5Cpi%7D%20%5C%2C%20.#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=c%20%3d%20%5cdfrac%7bv%5b(p-2)%5cpi%20%2b%202%5d%7d%7bm%5cpi%7d%20%5c%2c%20.#0


id: 10_1101-2021_02_13_429885
pos: NNP
token:
  https://www.codecogs.com/eqnedit.php?latex=v%20%3D%20%5Cdfrac%7Bv%5B(p-2)%5Cpi%20%2B%202%5D%7D%7Bm%5Cpi%7D%20#0

lemma:
  https://www.codecogs.com/eqnedit.php?latex=v%20%3D%20%5Cdfrac%7Bv%5B(p-2)%5Cpi%20%2B%202%5D%7D%7Bm%5Cpi%7D%20#0


id: 10_1101-2021_02_12_430764
pos: NNPS
token:
  http://arxiv.org/abs/https://academic.oup.com/bioinformatics/article-pdf/23/19/2507/452322/btm344.pdf

lemma:
  http://arxiv.org/abs/https://academic.oup.com/bioinformatics/article-pdf/23/19/2507/452322/btm344.pdf


id: 10_1101-2020_01_28_923532
pos: NNP
token:
  https://www.ncbi.nlm.nih.gov/pubmed/?term=Jameson%20GS%5BAuthor%5D&cauthor=true&cauthor_uid=25209003

lemma:
  https://www.ncbi.nlm.nih.gov/pubmed/?term=Jameson%20GS%5BAuthor%5D&cauthor=true&cauthor_uid=25209003


id: 10_1101-2021_02_10_430656
pos: NNP
token:
  https://github.com/pachterlab/BP_2021/blob/e87e98713bf7967d2fa22716dbbebd10609c1dd9/analysis/scripts/code/gsea_bar_full.R#L39

lemma:
  https://github.com/pachterlab/BP_2021/blob/e87e98713bf7967d2fa22716dbbebd10609c1dd9/analysis/scripts/code/gsea_bar_full.R#L39


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=s4aw0ujvuwn8

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=s4aw0ujvuwn8


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=14ruyzz6p81

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=14ruyzz6p81


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=i7cha45y61s6

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=i7cha45y61s6


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=pwzzqt446v1

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=pwzzqt446v1


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=2glf5ij7qkb6

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=2glf5ij7qkb6


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=mtxpwbv8o1fn

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=mtxpwbv8o1fn


id: 10_1101-2021_02_10_430705
pos: VBZ
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=gsc2l2p4cfhh

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=gsc2l2p4cfhh


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=e9uxoipvwota

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=e9uxoipvwota


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=onqq15xmlfrf

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=onqq15xmlfrf


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=jbvdrwuod0wd

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=jbvdrwuod0wd


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=igxr9liha0sr

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=igxr9liha0sr


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=s2f3i1x6uus6

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=s2f3i1x6uus6


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=ic935kiviuls

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=ic935kiviuls


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=c4gwvsmlicg

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=c4gwvsmlicg


id: 10_1101-2021_02_10_430705
pos: IN
token:
[cell repeats an earlier one in this file: 112 characters]

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=mtxpwbv8o1fn


id: 10_1101-2021_02_10_430705
pos: JJ
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=dt4acr7aal4q

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=dt4acr7aal4q


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hpu96rs05k0s

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hpu96rs05k0s


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=y5ixemnm3tac

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=y5ixemnm3tac


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=9ohfvgceg55

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=9ohfvgceg55


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=s2mv1z2n9zoj

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=s2mv1z2n9zoj


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hn4iy4esq5sd

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hn4iy4esq5sd


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=thb0kc2o9554

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=thb0kc2o9554


id: 10_1101-2021_02_10_430705
pos: VB
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=cpy816x2qcr

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=cpy816x2qcr


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=29jzsvekzs4z

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=29jzsvekzs4z


id: 10_1101-2021_02_10_430705
pos: RB
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=1ezm5xa09jlh

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=1ezm5xa09jlh


id: 10_1101-2021_02_10_430705
pos: ''
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=tbqfcii5qr09

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=tbqfcii5qr09


id: 10_1101-2021_02_10_430705
pos: WRB
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=wlju6tjkbefs

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=wlju6tjkbefs


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=qj4499p96ubk

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=qj4499p96ubk


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=z8feu0u2i2yt

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=z8feu0u2i2yt


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=d11sb02yalb

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=d11sb02yalb


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=nlu4dvpyxpr5

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=nlu4dvpyxpr5


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=w4963vuo4u2f

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=w4963vuo4u2f


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=uzyf68ddp60p

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=uzyf68ddp60p


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=fi93hnl2oym0

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=fi93hnl2oym0


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hy8nvy7h6bta

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hy8nvy7h6bta


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=c4deqc1by20h

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=c4deqc1by20h


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=6th1gotw3ydi

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=6th1gotw3ydi


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=l3wpb1nev23n

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=l3wpb1nev23n


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=xpkwepv274v

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=xpkwepv274v


id: 10_1101-2021_02_10_430705
pos: FW
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=mfw3e1p8x8hj

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=mfw3e1p8x8hj


id: 10_1101-2021_02_10_430705
pos: VB
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hpu96rs05k0s

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=hpu96rs05k0s


id: 10_1101-2021_02_10_430705
pos: FW
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=s9izyt6361ye

lemma:
  https://docs.google.com/document/d/17hkqd3b5gmabgnhrduyigpd_-ue37ofupuwd5jhxsou/edit#smartreference=s9izyt6361ye


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=gqvc4cw37qlq

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=gqvc4cw37qlq


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=8w1hbpz1mcwd

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=8w1hbpz1mcwd


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=4l8bltp8p9u2

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=4l8bltp8p9u2


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hkri0zw1jhb3

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hkri0zw1jhb3


id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=msgdsdsh4sty

lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=msgdsdsh4sty


id: 10_1101-2021_02_10_430705
pos: NNP
token:
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lemma:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=qiw3flfp2a4l


id: 10_1101-2021_02_10_430705
pos: NNP
token:
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lemma:
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id: 10_1101-2021_02_10_430705
pos: NNP
token:
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lemma:
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id: 10_1101-2021_02_10_430705
pos: NNP
token:
  https://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=k1dnflcadtcx

lemma:
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id: 10_1101-2021_02_10_430563
pos: .
token:
  https://commonfund.nih.gov/sites/default/files/SPARC_material%2520sharing%2520policy%252026jan17_508.pdf

lemma:
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id: 10_1101-2021_02_10_430563
pos: XX
token:
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lemma:
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id: 10_1101-2021_02_08_430275
pos: NNPS
token:
  https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels-

lemma:
  https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels-


id: 10_1101-2020_09_02_279521
pos: VB
token:
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lemma:
  http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/adni_acknowledgement_textunderscore%20list.pdf


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domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=8w1hbpz1mcwd


id: 10_1101-2021_02_10_430705
domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=4l8bltp8p9u2


id: 10_1101-2021_02_10_430705
domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=hkri0zw1jhb3


id: 10_1101-2021_02_10_430705
domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=msgdsdsh4sty


id: 10_1101-2021_02_10_430705
domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=qiw3flfp2a4l


id: 10_1101-2021_02_10_430705
domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=v1z8vlisxo3y


id: 10_1101-2021_02_10_430705
domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=7jdtkd13bs9a


id: 10_1101-2021_02_10_430705
domain: docs.google.com
url:
  http://docs.google.com/document/d/17hKqD3B5gmaBgnhrDUYigPD_-Ue37OFupUwD5jHxSOU/edit#smartreference=k1dnflcadtcx


id: 10_1101-2021_02_10_430563
domain: commonfund.nih.gov
url:
  http://commonfund.nih.gov/sites/default/files/SPARC_material%2520sharing%2520policy%252026jan17_508.pdf


id: 10_1101-2021_02_08_430275
domain: gatk.broadinstitute.org
url:
  http://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels-


# bib

id: 10_1101-2020_09_21_305516
title: Copy-scAT: Deconvoluting single-cell chromatin accessibility of genetic subclones in cancer
author: Nikolic, Ana
date: 2021
txt: ./txt/10_1101-2020_09_21_305516.txt
cache: ./cache/10_1101-2020_09_21_305516.pdf
extension: .pdf
mime: application/pdf
summary:
  Copy-scAT: Deconvoluting single-cell chromatin accessibility of genetic subclones in cancer 1 Copy-scAT: Deconvoluting single-cell chromatin accessibility of genetic subclones in cancer 1 uses single-cell epigenomic data to infer copy number variants (CNVs) that define cancer cells. We have tested the ability of Copy-scAT to use scATAC data to call CNVs with three different approaches 100 genome sequencing (WGS) data for adult GBM (aGBM) surgical resections (n = 4 samples, 3,647 cells). adult GBM samples identified using both methods, versus total numbers of gains detected by scATAC or 160 Number of chromosome-arm level gains detected in adult GBM samples identified using both methods, 163 (c) Multiple myeloma samples were profiled by both scATAC and the single-cell CNV assay. chromosome-arm level gains detected in adult GBM samples identified using both methods, versus total 166 CNVs are detected in scATAC clusters with Copy-scAT in pediatric GBM samples.


id: 10_1101-2021_02_10_430649
title: Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data
author: Wen, Zi-Hang
date: 2021
txt: ./txt/10_1101-2021_02_10_430649.txt
cache: ./cache/10_1101-2021_02_10_430649.pdf
extension: .pdf
mime: application/pdf
summary:
  Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion We introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk Bfimpute achieves better accuracy than other six publicly notable scRNA-seq imputation methods on simulated Key words: single cell; RNA-seq; imputation; Bayesian factorization impute dropout events by adopting the bulk RNA-seq data imputation of single cell RNA-seq data could be applied by Bfimpute recovers dropout values and improves cell type identification in the simulated data. and the imputed data by Bfimpute, scImpute, and DrImpute for the human embryonic stem cell differentiation study. imputation method scimpute for single-cell rna-seq data.


id: 10_1101-2021_02_12_431018
author: Truong Nguyen, Phuoc
date: 2021
txt: ./txt/10_1101-2021_02_12_431018.txt
cache: ./cache/10_1101-2021_02_12_431018.pdf
extension: .pdf
mime: application/pdf
summary:
  HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences. HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage 2 Several new variants of SARS-CoV-2 have emerged globally, of which the 18 based assemblies on raw SARS-CoV-2 sequences in addition to identifying lineages to detect 26 variants of concern, we have developed an open source bioinformatic pipeline called HaVoC 27 monitor the spread of SARS-CoV-2 variants of concern during local outbreaks. currently being used in Finland for monitoring the spread of SARS-CoV-2 variants. SARS-CoV2, variant detection, reference assembly, lineage identification, coronavirus, 40 surveillance of virus variants by sequencing the SARS-CoV-2 genomes would provide a fast 80 to query SARS-CoV-2 fastq sequence libraries and assigns lineages to them individually in 92 processing and a reference genome of SARS-CoV-2 in a separate FASTA file. The likelihood of emergence of novel SARS-CoV-2 variants of concern is increased and 209 Emerging SARS-CoV-2 Variants.

title:
  HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences.


id: 10_1101-2021_02_11_430847
author: Pinatti, Lisa M.
date: 2021
txt: ./txt/10_1101-2021_02_11_430847.txt
cache: ./cache/10_1101-2021_02_11_430847.pdf
extension: .pdf
mime: application/pdf
summary:
  SearcHPV: a novel approach to identify and assemble human papillomavirus-host genomic integration events in cancer squamous cell carcinomas; however, the impact of HPV integration into the host human genome SearcHPV uncovered HPV integration sites adjacent to known cancer-related detection of HPV-human integration sites from targeted capture DNA sequencing data. developed a novel HPV integration detection tool for targeted capture sequencing data, which we SearcHPV showed a high frequency of HPV16 integration with a total of six events in UM-SCCIn this study, SearcHPV also called HPV integration sites within TP63. HPV integration sites have been associated with structural variations in the human genome3, 8, 37, which supports an additional genetic mechanism as to why HPV integration sites Genome-wide analysis of HPV integration in human and their integration sites in host genomes through next generation sequencing data. identify viruses and their integration sites using next-generation sequencing of human cancer 

title:
  SearcHPV: a novel approach to identify and assemble human papillomavirus-host genomic integration events in cancer


id: 10_1101-2021_02_11_430871
author: Vadnais, David
date: 2021
txt: ./txt/10_1101-2021_02_11_430871.txt
cache: ./cache/10_1101-2021_02_11_430871.pdf
extension: .pdf
mime: application/pdf
summary:
  ParticleChromo3D: A Particle Swarm Optimization Algorithm for Chromosome and Genome 3D Structure Prediction from Hi-C Data chromosome and genome structure reconstruction from Hi-C data using Particle Swarm Optimization approach chromosome bin, according to the particle swarm algorithm, and then iterates its position towards a global best This paper presents ParticleChromo3D, a new distance-based algorithm for chromosome 3D structure The structures generated by ParticleChromo3D also shows that the result at swarm size Structures generated by ParticleChromo3D at different swarm size values. obtained by comparing the ParticleChromo3D algorithm''s output structure to the simulated dataset''s true plot of ParticleChromo3D SCC performance on 500KB GM12878 cell Hi-C data for chromosome 1 to 23. plot of ParticleChromo3D SCC performance on 500KB GM12878 cell Hi-C data for chromosome 1 to 23. chromosome 3D structure reconstruction algorithms on the GM12878 data set at both the 1MB and 500KB chromosome and genome structures reconstructed from Hi-C data.

title:
  ParticleChromo3D: A Particle Swarm Optimization Algorithm for Chromosome and Genome 3D Structure Prediction from Hi-C Data


id: 10_1101-2021_02_12_430963
title: Streamlining differential exon and 3'' UTR usage with diffUTR
author: Gerber, Stefan
date: 2021
txt: ./txt/10_1101-2021_02_12_430963.txt
cache: ./cache/10_1101-2021_02_12_430963.pdf
extension: .pdf
mime: application/pdf
summary:
  adenylation site databases to enable differential 3'' UTR usage analysis. Conclusions: diffUTR enables differential 3'' UTR analysis and more generally facilitates DEU9 Popular bin-based DEU methods are provided by the limma [25,24], edgeR [23] and DEXSeq [22]41 Bins are prepared from various types of gene annotations as well as, optionally, additional APA-driven segmentation and extension, then read counts among statistically-significant genes, especially for bins with a higher expression (Figure 3A).78 diffUTR provides three main plot types to explore differential bin usage analyses, each with a88 Plotted are the UTR bins found statistically significant (binand gene-level FDR deuBinPlot (Figure 4B) provides bin-level statistic plots for a given gene, similar to those99 than CDS bins, including counts of 3'' UTR when calculating overall gene expression could under-121 diffUTR streamlines DEU analysis and outperforms alternative methods in inferring UTR changes,127 For differential UTR analysis, gene-level results are ob-206


id: 10_1101-2021_02_13_429885
author: Househam, Jacob
date: 2021
txt: ./txt/10_1101-2021_02_13_429885.txt
cache: ./cache/10_1101-2021_02_13_429885.pdf
extension: .pdf
mime: application/pdf
summary:
  know tumour purity and the ploidy of a CNA segment, then the VAF mutations mapped A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing.

title:
  A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing


id: 10_1101-2021_02_12_430830
author: Gergely, Tibély
date: 2021
txt: ./txt/10_1101-2021_02_12_430830.txt
cache: ./cache/10_1101-2021_02_12_430830.pdf
extension: .pdf
mime: application/pdf
summary:
  Simultaneous estimation of per cell division mutation rate and turnover rate from bulk tumor sequence data widely available bulk sequencing data where mutations from individual cells are and genomic mutation rate from bulk sequencing data. based on the maximum likelihood estimation of the parameters of a generative model of tumor growth and mutations. human hepatocellular carcinoma sample reveals an elevated per cell division mutation rate and high cell turnover. Due to the limitations of bulk sequencing, which only essays mutation frequencies for a population of cells from each tumor sample and does not The estimation is based on a maximum likelihood fit of the parameters of a birth-death model to the measured mutant and be estimated from readcount data, to separate the effects of the mutation rate We use pre-generated division trees from the ELynx suite at predetermined turnover rate values. Using the turnover rate, we also estimated the number of cell

title:
  Simultaneous estimation of per cell division mutation rate and turnover rate from bulk tumor sequence data


id: 10_1101-2021_02_12_430739
title: Mutations in bdcA and valS correlate with quinolone resistance in wastewater Escherichia Coli
author: Malekian, Negin
date: 2021
txt: ./txt/10_1101-2021_02_12_430739.txt
cache: ./cache/10_1101-2021_02_12_430739.pdf
extension: .pdf
mime: application/pdf
summary:
  Mutations in bdcA and valS correlate with quinolone resistance in wastewater Escherichia Coli Here, we systematically screen for candidate quinolone resistance-conferring mutations. coli and performed a genome-wide association study (GWAS) correlating over 200,000 mutations against quinolone resistance phenotypes. significant mutations including one located at the active site of the biofilm dispersal genes bdcA and six silent In summary, we demonstrate that GWAS effectively and comprehensively identifies resistance mutations Keywords: E Coli; Quinolone; Antibiotic Resistance; Genome-Wide Association Study (GWAS) direct route to resistance is mutations in the drug targets gyrA and parC. In summary, we aim to show that a bacterial genomewide association study can effectively and comprehensively identify targets relevant to antibiotic resistance. Based on representative resistance phenotypes, the authors selected 103 isolates for sequencing with Illumina MiSeq, 92 of which are available from coli bdcA may act indirectly on antibiotic resistance.


id: 10_1101-2021_02_12_430764
title: Triku: a feature selection method based on nearest neighbors for single-cell data
author: Ascensión, Alex M.
date: 2021
txt: ./txt/10_1101-2021_02_12_430764.txt
cache: ./cache/10_1101-2021_02_12_430764.pdf
extension: .pdf
mime: application/pdf
summary:
  Triku: a feature selection method based on nearest neighbors for single-cell data Triku is a feature selection method that favours genes defining the main Single-cell RNA sequencing (scRNA-seq) is a powerful technology to study the biological heterogeneity of tissues at the individual cell level, allowing the characterization of new cell populations and cell states–i.e. cell types responding to different scRNA-seq datasets are multidimensional, i.e. the expression profile per cell consists of multiple genes. feature selection method: 1) the ability to recover basic dataset structure (main cell low, meaning that features selected with the different methods yielded clustering solutions that were quite similar to the manually-labeled cell types, although there are We first studied the expression pattern of genes selected by triku and other methods, To evaluate the cluster expression of selected genes in benchmarking datasets, for proteins within the genes selected by different FS methods in the two sets of benchmarking datasets.


id: 10_1101-2020_01_28_923532
title: The Landscape of Precision Cancer Combination Therapy: A Single-Cell Perspective
author: Ahmadi, Saba
date: 2021
txt: ./txt/10_1101-2020_01_28_923532.txt
cache: ./cache/10_1101-2020_01_28_923532.pdf
extension: .pdf
mime: application/pdf
summary:
  We focus our analysis on genes encoding protein targets that encode receptors on the cell all "modular", including one part that specifically targets the tumor cell via one gene/protein and MadHitter and each patient receives an optimal personalized combination of targeted therapies from a prespecified set (pill bottle). Cohort and Individual Target Set Sizes as Functions of Tumor Killing and Given the single-cell tumor data sets and the ILP optimization framework described above, we filtering as this threshold is decreased), decreases the size of the target cell surface receptor gene heterogeneity of the cancer, number of patients within the data set, size of target gene set, lack of used for filtering the gene set to avoid targeting non-cancerous tissues. the genes in the optimal target set, the expression of that gene in that non-tumor cell exceeds the set of genes which is known to be targetable to cell 𝐶.


id: 10_1101-2021_02_11_430762
title: Ribovore: ribosomal RNA sequence analysis for GenBank submissions and database curation
author: Schäffer, Alejandro A.
date: 2021
txt: ./txt/10_1101-2021_02_11_430762.txt
cache: ./cache/10_1101-2021_02_11_430762.pdf
extension: .pdf
mime: application/pdf
summary:
  Ribovore: ribosomal RNA sequence analysis for GenBank submissions and database curation alignments of SSU, LSU and 5S rRNA from all three domains as well as from organelles, along with secondary structure predictions for selected sequences. Ribovore software package for the analysis of SSU rRNA and LSU rRNA sequences 18S SSU rRNA database of 1091 sequences was updated most recently on September 27, 2018 by running version 0.28 of the Ribovore program ribodbmaker on an input set of 579,279 GenBank sequences returned from the eukaryotic SSU rRNA The results of ribotyper and rRNA sensor are combined and each sequence is separated into one of four outcome classes depending on whether it passed or failed each input a set of candidate sequences and a specified rRNA model (e.g. SSU.Bacteria) two blastn databases: one of 1267 bacterial and archaeal 16S SSU rRNA sequences


id: 10_1101-2020_09_23_308239
author: Schultz, Bruce T
date: 2021
txt: ./txt/10_1101-2020_09_23_308239.txt
cache: ./cache/10_1101-2020_09_23_308239.pdf
extension: .pdf
mime: application/pdf
summary:
  The COVID-19 PHARMACOME: A method for the rational selection of drug repurposing COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a initial version of the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph representing COVID-19 pathophysiology mechanisms that includes both drug targets Figure 3: Overlap of compound hits between different drug repurposing screening experiments. space overlap between different COVID-19 drug repurposing screenings. The COVID-19 PHARMACOME associates pathways derived from drug repurposing targets Figure 4 shows the distribution of repurposing drugs in the COVID-19 cause-and-effect graph, overlap analysis allows for the identification of repurposing drugs targeting mechanisms that Virus-response mechanisms are targets for repurposing drugs Figure 5: Visualization of drug repurposing candidates (and their targets) used in combination treatment as our own drug repurposing screening results, we were able to identify mechanisms targeted COVID-19 PHARMACOME, we are now able to link repurposing drugs, their targets and the SARS-CoV-2 protein interaction map reveals targets for drug repurposing.

title:
  The COVID-19 PHARMACOME: A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization


id: 10_1101-2020_09_23_310276
author: Greenfest-Allen, Emily
date: 2021
txt: ./txt/10_1101-2020_09_23_310276.txt
cache: ./cache/10_1101-2020_09_23_310276.pdf
extension: .pdf
mime: application/pdf
summary:
  The NIAGADS Alzheimer''s Genomics Database (GenomicsDB) is an interactive knowledgebase for Alzheimer''s disease (AD) genetics that provides access to GWAS summary statistics datasets The website makes available >70 genome-wide summary statistics datasets from GWAS and efficient real-time data analysis and variant or gene report generation. Gene reports provide summaries of co-located ADRD risk-associated variants and have pages linking summary statistics to variant and gene annotations, this resource makes these summary statistics available for browsing (on dataset, gene, and variant reports and as genome NIAGADS GenomicsDB variant reports and a track is available on the genome browser. The NIAGADS GenomicsDB includes allele frequency data from 1000 Genomes (phase 3, version visualizations for summarizing search results and annotations in gene and variant reports. compare NIAGADS GWAS summary statistics tracks to each other, against annotated gene or A detailed report is provided for each of the GWAS summary statistics and ADSP meta-analysis 

title:
  NIAGADS Alzheimer''s GenomicsDB: A resource for exploring Alzheimer''s Disease genetic and genomic knowledge


id: 10_1101-2021_02_11_430806
title: BIAPSS - BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences
author: Badaczewska-Dawid, Aleksandra
date: 2021
txt: ./txt/10_1101-2021_02_11_430806.txt
cache: ./cache/10_1101-2021_02_11_430806.pdf
extension: .pdf
mime: application/pdf
summary:
  BIAPSS BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences web platform named BIAPSS (BioInformatic Analysis of liquidliquid Phase-Separating protein Sequences) which offers the users interactive data analytic tools for facilitating the discovery of statistically significant sequence signals for proteins with Phase-Separating protein Sequences. The objective of BIAPSS is to enable a rapid and on-the-fly deep statistical analysis of LLPS-driver proteins using the pool of sequences with The comparison to benchmarks of various protein groups enables statistical inference of specific phase-separating affinities. Furthermore, the residue-resolution biophysical regularities inferred from BIAPSS will help not only to accurately identify regions prone to phase separation but also to design sequence modifications targeting various biomedical applications. for comprehensive sequence-based analysis of LLPS proteins. the driving forces for phase separation of prion-like RNA binding proteins. disordered protein regions encode a driving force for liquid-liquid phase separation? of proteins driving liquid-liquid phase separation.


id: 10_1101-2021_02_11_430695
title: Learning Sparse Log-Ratios for High-Throughput Sequencing Data
author: Gordon-Rodriguez, Elliott
date: 2021
txt: ./txt/10_1101-2021_02_11_430695.txt
cache: ./cache/10_1101-2021_02_11_430695.pdf
extension: .pdf
mime: application/pdf
summary:
  Log-ratios are an important class of features for analyzing high-throughput sequencing (HTS) metagenomic data for HTS data, and more generally, high-dimensional CoDa. Unlike existing methods, CoDaCoRe is simultaneously scalable, interpretable, sparse, and accurate. unlabelled datasets, {xi}ni=1, as a method for identiLearning Sparse Log-Ratios for High-Throughput Sequencing Data CoDaCoRe variable selection for the first (most explanatory) log-ratio on the Crohn disease data (Rivera-Pinto et al., 2018). more generally, in the field of CoDa. Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data Learning Sparse Log-Ratios for High-Throughput Sequencing Data


id: 10_1101-2021_02_12_430989
title: Benchmarking Association Analyses of Continuous Exposures with RNA-seq in Observational Studies
author: Sofer, Tamar
date: 2021
txt: ./txt/10_1101-2021_02_12_430989.txt
cache: ./cache/10_1101-2021_02_12_430989.pdf
extension: .pdf
mime: application/pdf
summary:
  Benchmarking Association Analyses of Continuous Exposures with RNA-seq in Observational Studies as well as linear regression-based analyses for studying the association of continuous exposures generation of empirical null distribution of association p-values, and we apply the pipeline to Many studies of phenotypes associated with gene expression from RNA-seq consist of small Residual permutation approach for simulations and for empirical p-value computation covariates, and outcome distributions; and (b) their relationships, aside from the exposureoutcome association, are the same as in the real data, we used a residual permutation approach. association studies applied to residual permutations were included to compute empirical papproach to study the distribution of p-values under the null of no association between the phenotypes and RNA-seq, and used this approach to further study power, and to compute approaches for transcriptome-wide analysis of RNA-seq in population-based studies, including more comprehensive study of statistical permutation approaches for RNA-seq association 


id: 10_1101-2021_02_12_430979
title: StrainFLAIR: Strain-level profiling of metagenomic samples using variation graphs
author: Da Silva, Kévin
date: 2021
txt: ./txt/10_1101-2021_02_12_430979.txt
cache: ./cache/10_1101-2021_02_12_430979.pdf
extension: .pdf
mime: application/pdf
summary:
  StrainFLAIR: Strain-level profiling of metagenomic samples using variation graphs results show that StrainFLAIR was able to distinguish and estimate the abundances of close strains, as approaches to handle multiple similar genomes as with strains use gene clustering and then select the64 StrainFLAIR assigns and estimates species and strain abundances of a bacterial metagenomic sample graph, called the "node abundance", is computed, first focusing on unique mapped reads (first step). Strain-level abundances are then obtained by exploiting the specific genes of each reference genome188 from the reference variation graph thus simulating a new strain to be identified and quantified.231 strains from a sequenced sample, mapped onto this graph.343 Reference strains relative abundances expected and computed by StrainFLAIR or Reference strains relative abundances expected and computed by StrainFLAIR or Reference strains relative abundances expected and computed by StrainFLAIR or Reference strains relative abundances expected and computed by StrainFLAIR or


id: 10_1101-2021_02_12_430923
title: Kincore: a web resource for structural classification of protein kinases and their inhibitors
author: Modi, Vivek
date: 2021
txt: ./txt/10_1101-2021_02_12_430923.txt
cache: ./cache/10_1101-2021_02_12_430923.pdf
extension: .pdf
mime: application/pdf
summary:
  Kincore: a web resource for structural classification of protein kinases and their inhibitors result, among the DFGin structures, we distinguished between the catalytically active kinase conformation pages for kinase phylogenetic groups, genes, conformational labels, PDBids, ligands and ligand types. options to download data – database tables as a tab separated files; the kinase structures as PyMOL Kincore provides conformational assignments and ligand type labels to protein kinase structures from Figure 1: Representative protein kinase structure (3ETA_A) displaying the residues used to define inhibitor The distribution of different ligand types across kinase conformations is provided in Table 1. Table 1: Distribution of ligand types across protein kinase conformations (Number of chains). including conformational and ligand type labels and C-helix position, kinase family, gene name, Uniprot provides the number of kinase chains in the group across different conformations with their Database table provides the list of all the PDB chains with conformational labels and ligand 


id: 10_1101-727867
title: scAEspy: a tool for autoencoder-based analysis of single-cell RNA sequencing data
author: Tangherloni, Andrea
date: 2021
txt: ./txt/10_1101-727867.txt
cache: ./cache/10_1101-727867.pdf
extension: .pdf
mime: application/pdf
summary:
  scAEspy: a tool for autoencoder-based analysis of single-cell RNA sequencing data This computational tool allows for coupling low-dimensional probabilistic representation of gene expression data with the downstream analysis to consider the Finally, the currently available AEs cannot be directly exploited to obtain the latent space or to generate synthetic cells. to show the cells in this embedded space or as a starting point for other dimensionality reduction approaches (e.g., t-SNE and UMAP) as well as downstream analyses Non-linear approaches for dimensionality reduction can be effectively used to capture the non-linearities among the gene interactions that may exist in the highdimensional expression space of scRNA-Seq data [16]. be effectively applied to analyse disparate types of single-cell data from different flexible method developed to cluster single-cell data; (ii) a centroid is calculated batch-effect correction methods for single-cell rna sequencing data. Wang, D., Gu, J.: VASC: dimension reduction and visualization of single-cell RNA-seq data by deep


id: 10_1101-2020_12_24_424317
title: Multi-class Cancer Classification and Biomarker Identification using Deep Learning
author: Muazzam, Fariha
date: 2021
txt: ./txt/10_1101-2020_12_24_424317.txt
cache: ./cache/10_1101-2020_12_24_424317.pdf
extension: .pdf
mime: application/pdf
summary:
  classification, feature extraction and relevant gene identification through deep learning methods for 12 This research picks up from detection of different types of cancer RNA-Seq expressions using deep neural classification of gene expression profiles for different kinds of cancers. Hence, the effectiveness of deep learning models for feature extraction and relevant gene identification is performed revealing substantial results and they produced five high-ranked gene sets and reduced feature This study was aimed at classifying 12 types of cancer and identifying relevant genes and the results show were able to identify cancer-relevant pathways and genes for the sets, that different experiments generated, A deep learning approach for cancer detection and relevant gene Tumor gene expression data classification via sample expansionbased deep learning. Identification of a multi-cancer gene expression Multi-class Cancer Classification and Biomarker Identification using Deep Learning Multi-class Cancer Classification and Biomarker Identification using Deep Learning


id: 10_1101-2020_10_08_327718
title: Creating Clear and Informative Image-based Figures for Scientific Publications
author: Jambor, Helena
date: 2021
txt: ./txt/10_1101-2020_10_08_327718.txt
cache: ./cache/10_1101-2020_10_08_327718.pdf
extension: .pdf
mime: application/pdf
summary:
  journals in three fields; plant sciences, cell biology and physiology (n=580 papers). figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). among papers published in top journals in plant sciences, cell biology and physiology. contained images (plant science: 68%, cell biology: 72%, physiology: 55%). in physiology (49%) and cell biology (55%), and 28% of plant science papers provided and 29% of plant sciences papers contained no scale information on any image. Some publications use insets to show the same image at two different scales (cell Figure 1: Image types and reporting of scale information and insets physiology and plant science papers contained some images that were inaccessible to B: Most papers explain colors in image-based figures, however, explanations are less Figure 4: Using scale bars to annotate image size Creating clear and informative image-based figures for scientific publications. Creating clear and informative image-based figures for scientific publications.


id: 10_1101-2021_02_09_430550
author: Song, Dongyuan
date: 2021
txt: ./txt/10_1101-2021_02_09_430550.txt
cache: ./cache/10_1101-2021_02_09_430550.pdf
extension: .pdf
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summary:
  (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Therefore, for scRNA-seq data analysis, informative gene selection Besides scRNA-seq data analysis, informative gene selection is also crucial for designing number and a scRNA-seq dataset, scPNMF selects informative genes based on its weight matrix; First, the informative genes selected by scPNMF lead to the most accurate cell clustering. the informative genes and weight matrix of scPNMF lead to the best cell type prediction accuracy Figure 3: Benchmarking scPNMF against 11 informative gene selection methods on seven scRNA-seq (b) UMAP visualization of cells in the Zheng4 dataset based on 100 informative genes selected by We benchmark scPNMF against the 11 gene selection methods in terms of cell type prediction We propose scPNMF, an unsupervised gene selection and data projection method for scRNA-seq For cell type prediction, we project every targeted gene profiling dataset and its scRNA-seq

title:
  scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling


id: 10_1101-2021_02_10_430656
author: Zakeri, Mohsen
date: 2021
txt: ./txt/10_1101-2021_02_10_430656.txt
cache: ./cache/10_1101-2021_02_10_430656.pdf
extension: .pdf
mime: application/pdf
summary:
  A like-for-like comparison of lightweight-mapping pipelines for single-cell RNA-seq data pre-processing benchmark comparing the kallisto-bustools pipeline (2) for single-cell demonstrate that, when configured to match the computational complexity of kallisto-bustools as closely as possible, alevin-fry processes Alevin-fry (3) is a new pipeline for single-cell RNA-seq benchmarking STARsolo (9), kallisto-bustools (2) and alevin-fry (3), out new tools like alevin-fry for the pre-processing of single-cell data, (1), we have now created a simple-to-follow tutorial for speedoptimized single-cell pre-processing using alevin-fry (https:// by Booeshaghi and Pachter (1) change when a like-for-like comparison between alevin-fry and kallisto-bustools is carried out, we The time and memory used by the relevant steps of the alevin-fry and kallisto-bustools pipelines for pre-processing the 20 diverse tagged-end single-cell RNA-seq datasets used in (1). A comparison of the resulting count matrices obtained from alevin-fry and kallisto-bustools, as run in this manuscript, for the pbmc_10k_v3 dataset. peak memory than alevin-fry, with the kallisto-bustools pipeline using

title:
  A like-for-like comparison of lightweight-mapping pipelines for single-cell RNA-seq data pre-processing


id: 10_1101-2021_02_10_430619
title: Cutevariant: a GUI-based desktop application to explore genetics variations
author: Schutz, Sacha
date: 2021
txt: ./txt/10_1101-2021_02_10_430619.txt
cache: ./cache/10_1101-2021_02_10_430619.pdf
extension: .pdf
mime: application/pdf
summary:
  Cutevariant: a GUI-based desktop application to explore genetics variations Cutevariant is a user-friendly GUI based desktop application for genomic research designed to search for variations in DNA samples collected in annotated files and encoded in the Variant Calling Format. application imports data into a local relational database wherefrom complex filter-queries can be built either Key words: genomics, DNA variant, desktop application, Domain Specific Language, Graphic User Interface applications import the data from VCF files into an indexed Cutevariant imports data from VCF files into a normalized Fig. 2: The Cutevariant main view showing the variants list sub-window (middle), different controllers sub-windows but not all are Just like Variant Tools, Cutevariant supports operations Features Cutevariant BrowseVCF VCF-Miner VCF-Explorer VCF-Server VCF-Filters GEMINI Variant Tools SnpSift Comparaison of time performance between cutevariant and VCF-miner for importation and query execution. 3. Pablo Cingolani, Adrian Platts, Le Lily Wang, Melissa VCF-Miner: GUI-based application for mining variants


id: 10_1101-2021_02_11_430789
title: Accelerating COVID-19 research with graph mining and transformer-based learning
author: Tyagin, Ilya
date: 2021
txt: ./txt/10_1101-2021_02_11_430789.txt
cache: ./cache/10_1101-2021_02_11_430789.pdf
extension: .pdf
mime: application/pdf
summary:
  Accelerating COVID-19 research with graph mining and transformer-based learning develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. is currently customized and available in the open domain to massively process COVID-19 related queries. Both systems are the next generation of the AGATHA knowledge network mining transformer model [37]. (1) Most of the existing HG systems are domain-specific (e.g., genedisease interactions) that is usually expressed in limiting the processed information (e.g., significant filtering vocabulary and papers a trained deep bi-LSTM model for extracting predicates from unstructured text. For instance, the node representing the entity "COVID-19" is connected to every sentence and predicate that The prior AGATHA semantic network only includes UMLS terms that appear in SemMedDB predicates [18] which is a major limitation. obtain embeddings per node in the semantic graph, we train AGATHA system ranking model.


id: 10_1101-2021_02_10_430512
author: Kim, Catherine
date: 2021
txt: ./txt/10_1101-2021_02_10_430512.txt
cache: ./cache/10_1101-2021_02_10_430512.pdf
extension: .pdf
mime: application/pdf
summary:
  into DDIs. In this study, a hierarchical machine learning model was created to predict DDIassociated ADRs and pharmacological insight thereof for any drug pair. drugs'' chemical structures as inputs to predict their target, enzyme, and transporter (TET) Development of RFCs for Prediction of Target, Enzyme, and Transporter Profiles of Drugs Development of a Model for Prediction of DDI-associated ADRs from TET Profiles of Drugs ADR prediction from Target, Enzyme, and Transporter Profiles of Drug Pairs To predict ADRs of a drug pair from its TET profiles, Random Forest Classifier (RFC), Application of the SVM model for DDI-associated ADRs Involving Three Major Drugs through predicted PRR changes of drug pairs upon removal of each of the targets, enzymes, and changes of drug pairs were predicted by the model upon removal of each of the targets, enzymes, Target, enzyme, and transporter (TET) profiles of atorvastatin and concomitant drugs, 

title:
  Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification


id: 10_1101-2021_02_10_430705
title: VIA: Generalized and scalable trajectory inference in single-cell omics data
author: Stassen, Shobana V.
date: 2021
txt: ./txt/10_1101-2021_02_10_430705.txt
cache: ./cache/10_1101-2021_02_10_430705.pdf
extension: .pdf
mime: application/pdf
summary:
  1 VIA: Generalized and scalable trajectory inference in single-cell omics data 1 VIA: Generalized and scalable trajectory inference in single-cell omics data 35 strategy to compute pseudotime, and reconstruct cell lineages based on lazy-teleporting random walks Step 1: Single-cell level graph is clustered such that each node 50 user defined start cell) is first computed by the expected hitting time for a lazy-teleporting random walk along an 57 network topology and single-cell level pseudotime/lineage probability properties onto an embedding using GAMs, as The cell fates and their lineage pathways are then computed by a two-stage probabilistic method, 94 graph-traversal allows it to infer cell fates when the underlying data spans combinations of multifurcating 201 detected cell fates annotated (o) lineage pathway and gene-pseudotime trend shown for the CD41 Megakaryocytic 259 Figure 3 VIA infers trajectories in single-cell multi-omic and image datasets (a) Major lineages of human Single cells are represented by graph nodes that are connected based on 


id: 10_1101-2021_02_10_430606
title: NeuronMotif: Deciphering transcriptional cis-regulatory codes from deep neural networks
author: Wei, Zheng
date: 2021
txt: ./txt/10_1101-2021_02_10_430606.txt
cache: ./cache/10_1101-2021_02_10_430606.pdf
extension: .pdf
mime: application/pdf
summary:
  Each point is a decoupled motif generate by a sample set of sequence. Only the max activation value of the decoupled motifs in Fig. 3b are significantly higher than the decoupled motifs of other neurons in layer 3 of Basset-3 model. discovered (q-value < 0.001) from the neuron in convolutional output layer of Basset, BD-5 and BD-10 model. c, The number of motif discovered (q-value < 0.01) from the neuron in layer 3 of Basset model using different sub-patterns in the input feature map of the max pooling layer to split the sequences set of which are DNA-sequence based DCNN models with 3 general convolutional layers for stacking sequences of different synonymous motifs with the maximum activation value In summary, we presented NeuronMotif as an effective algorithm to reveal the cisregulatory motif grammar learned by DCNN model that use DNA sequence to annotate sequences indicate more synonymous motif mixture in this DCNN model.


id: 10_1101-698605
title: Comparative evaluation of full-length isoform quantification from RNA-Seq
author: Sarantopoulou, Dimitra
date: 2021
txt: ./txt/10_1101-698605.txt
cache: ./cache/10_1101-698605.pdf
extension: .pdf
mime: application/pdf
summary:
  Comparative evaluation of full-length isoform quantification from RNA-Seq Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses benchmarking, isoform quantification, simulated data, pseudo-alignment, RNA-Seq, short Given the difficulty in full-length isoform quantification, many RNA-Seq studies simply analysis performed on the known true isoform quantifications of the simulated data to the For the simulated data we started with 11 real RNA-Seq samples: six liver and six the isoform expression level using idealized and realistic simulated data, with full and true counts), for the set of expressed isoforms in sample 1 in C) idealized and D) realistic data. Method effect on differential expression analysis, using realistic data. Method effect on differential expression analysis, using realistic data. RSEM is a gene/isoform abundance tool for RNA-Seq data which uses a generative model S1 Fig. Method effect on full-length isoform quantification using simulated data. Method effect on full-length isoform quantification using simulated data.


id: 10_1101-2021_02_10_430563
title: SPARC Data Structure: Rationale and Design of a FAIR Standard for Biomedical Research Data
author: Bandrowski, Anita
date: 2021
txt: ./txt/10_1101-2021_02_10_430563.txt
cache: ./cache/10_1101-2021_02_10_430563.pdf
extension: .pdf
mime: application/pdf
summary:
  investigators across the SPARC consortium that provide key details about organ-specific circuitry, including structural (BIDS), the SDS has been designed to capture the large variety of data generated by SPARC investigators who are description of the SPARC curation process and the automated tools for complying with the SDS, including the SDS validator and Software to Organize Data Automatically (SODA) for SPARC. required to organize their data files and metadata organized according to the SPARC Data Structure data according to the SPARC Dataset Structure. is the preferred file format for tabular data in SPARC, the Data files are organized into 3 different top-level folders, The organization structure of the files and folders for a SPARC dataset. https://github.com/SciCrunch/sparc-curation/releases/tag/dataset-template-1.2.3 https://github.com/SciCrunch/sparc-curation/releases/tag/dataset-template-1.2.3 investigators include folders that organize data along a from these subjects, data files are organized within fields, the curation team developed a SPARC Dataset files/folders, and share datasets with the SPARC 


id: 10_1101-2020_11_17_386649
title: Topology-based Sparsification of Graph Annotations
author: Danciu, Daniel
date: 2021
txt: ./txt/10_1101-2020_11_17_386649.txt
cache: ./cache/10_1101-2020_11_17_386649.pdf
extension: .pdf
mime: application/pdf
summary:
  Experiments on 10,000 RNA-seq datasets show that RowDiff combined with MultiBRWT results in a 30% reduction in annotation footprint over Mantis-MST, the previously known most a binary matrix, where the k-mer set indexes the rows and each annotation label specifies a column. Starting from any vertex in the de Bruijn graph, Algorithm 1 defines a traversal leading to an anchor Each row in a RowDiff-transformed annotation matrix has the same or fewer set bits than A naı̈ve implementation of the RowDiff construction would be to load the matrix A in memory, and gradually replace its rows with their sparsified counterpart, while traversing the graph. We now note that, when querying annotations for paths in the graph, or sets of rows corresponding to vertices We constructed annotated de Bruijn graphs from the RNA-Seq data set in the same We now compare the representation size for RowDiff and other state-of-the-art graph annotation compression methods.


id: 10_1101-2020_05_15_090266
title: SpacePHARER: Sensitive identification of phages from CRISPR spacers in prokaryotic hosts
author: Zhang, R.
date: 2021
txt: ./txt/10_1101-2020_05_15_090266.txt
cache: ./cache/10_1101-2020_05_15_090266.pdf
extension: .pdf
mime: application/pdf
summary:
  Summary: SpacePHARER (CRISPR Spacer Phage-Host Pair Finder) is a sensitive and fast tool for de novo prediction of phage-host relationships via identifying phage genomes that match CRISPR spacers in genomic or metagenomic data. SpacePHARER gains sensitivity by comparing spacers and phages at the protein level, optimizing its scores for matching SpacePHARER by searching a comprehensive spacer list against all complete phage genomes. methods compare individual CRISPR spacers with phage To increase sensitivity, (1) we compare protein coding sequences because phage genomes are mostly coding, and, (0) Preprocess input: scan the phage genome and CRISPR spacers in six ORFs q of CRISPR spacers extracted from one prokaryotic genome, and each target set T comprises the putative protein sequences t from a single phage. The performance of SpacePHARER was evaluated on the spacer test set against a target database predicted the correct host for more phages than BLASTN BLASTN in detecting phage-host pairs, due to searching


id: 10_1101-2021_02_01_429246
title: Sequence-specific minimizers via polar sets
author: Zheng, Hongyu
date: 2021
txt: ./txt/10_1101-2021_02_01_429246.txt
cache: ./cache/10_1101-2021_02_01_429246.pdf
extension: .pdf
mime: application/pdf
summary:
  minimizers focus on sampling fewer k-mers on a random sequence and use universal hitting sets (sets suggests, a UHS is a set of k-mers that "hits" every w-long window of every possible sequence (hence the the elements of the polar sets are in the sequence: the higher the energy, the more spread apart the k-mers have densities upper bounded by |U|/σk, because only k-mers from the universal hitting set can be selected. Section 2.2 gives a formal definition of the link energy of a polar set and Theorem 1 gives upper and lower bounds using this link energy for the density of a minimizer compatible with a polar set. form a link, which in turn is the number of k-mer pairs in the polar set that are exactly w bases away on S. A context is charged if the minimizer selects a different k-mer in the first window than in the second


id: 10_1101-2021_02_10_430623
author: Aberasturi, Dillon
date: 2021
txt: ./txt/10_1101-2021_02_10_430623.txt
cache: ./cache/10_1101-2021_02_10_430623.pdf
extension: .pdf
mime: application/pdf
summary:
  published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g., diseased vs unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-variance using these models to derive effect sizes and statistical significance in singlesubject studies of transcriptomes, these samples are isogenic or quasi-isogenic, and thus do not necessarily generalize to a group of subjects (cohort-level signal). The novel bioinformatic method identifies meaningful biomechanism differences between very small cohorts by using single-subject-study-derived effect sizes for gene sets. (B) For the generalized linear model-based analyses, we applied a different filtering process to the raw data where we eliminated all the transcripts with 0 counts for each subject and then calculated the coefficient 2.3 Description of the Generalized Linear Models and application of Inter-N-of-1 methods for small cohort comparison and their evaluation in the Breast Cancer Data the analysis of subsets of the TCGA Breast Cancer data, genes were declared differentially expressed if their abs(log2FC) > log2(1.2) and their FDR-adjusted p-value < 

title:
  “Single-subject studies”-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases


id: 10_1101-2021_02_09_430405
author: Quazi, Sameer
date: 2021
txt: ./txt/10_1101-2021_02_09_430405.txt
cache: ./cache/10_1101-2021_02_09_430405.pdf
extension: .pdf
mime: application/pdf
summary:
  In-silico Structural and Molecular Docking-Based Drug Discovery Against Viral Protein (VP35) of Marburg Virus: A potent Agent of MAVD including structure-based drug-like compounds screening from online databases, molecular The final small molecules of drug-like compounds would have more effective and selected for the molecular docking with FGI-103 antiviral drug-using AutoDock 4.2 software. After that, FGI-103 was set and screen other drug-like compounds from PubChem databases. The finally selected drug-like compounds were docked with the P1 site of VP35 of based on ap1 site for ligand in every dock for VP35 MARV utilizing a grid chart of 50 × 50 × 50 The ADMET properties of finally selected drug-like compounds were checked to utilize 2D molecules structure of selected drug-like compounds (A) represents the 2D The molecule structure of three drug-like compounds is shown in Figure 6. "In-Silico Structural and Molecular Docking-Based Drug Discovery "In-Silico Structural and Molecular Docking-Based Drug Discovery 

title:
  <em>In-silico</em> Structural and Molecular Docking-Based Drug Discovery Against Viral Protein (VP35) of Marburg Virus: A potent Agent of MAVD


id: 10_1101-2021_02_10_430367
title: Genome Warehouse: A Public Repository Housing Genome-scale Data
author: Chen, Meili
date: 2021
txt: ./txt/10_1101-2021_02_10_430367.txt
cache: ./cache/10_1101-2021_02_10_430367.pdf
extension: .pdf
mime: application/pdf
summary:
  Running title: Chen M et al / Genome Assembly Data Repository 21 Genomics Data Center (NGDC), part of the China National Center for Bioinformation 40 archive high-quality genome sequences and annotations, GWH is equipped with a 46 Collectively, GWH serves as an important resource for genome-scale data 51 https://bigd.big.ac.cn/) [13], the aim of GWH is to accept data submissions worldwide 78 GWH is a centralized resource housing genome-scale data, with the purpose to 105 GWH not only accepts genome assembly associated data through an on-line 111 GWH will assign a unique accession number to the submitted genome assembly upon 149 GWH provides data visualization for both genome 163 Collectively, GWH is a user-friendly portal for genome data submission, release, and 209 Database resources of the National Genomics Data 302 Genome assembly accession number is prefixed with "GWH", followed by four 334 Genome assembly accession number is prefixed with "GWH", followed by four 334 


id: 10_1101-2021_02_09_430536
author: Lin, Cui-Xiang
date: 2021
txt: ./txt/10_1101-2021_02_09_430536.txt
cache: ./cache/10_1101-2021_02_09_430536.pdf
extension: .pdf
mime: application/pdf
summary:
  Genome-wide prediction and integrative functional characterization of Alzheimer''s disease-associated genes example, a module-trait network approach was proposed and applied to identify gene 63 functional enrichment-based approach to identify negative genes that are not likely 94 associated genes through an optimal selection of networks and machine learning 98 FGN, and prediction of AD-associated genes using machine learning models (Fig. 1). addition, we tested their enrichment in three AD-related gene sets associated with 122 The top-ranked genes are enriched in AD-associated functions and phenotypes 154 These results provide additional evidence that our predicted genes are associated with 194 The top-ranked genes are associated with AD based on miRNA-target networks 227 We investigated whether top-ranked genes were functionally related to AD-associated 229 We tested whether the top-ranked k genes were more likely to interact with AD-associated 576 related to AD-associated genes or miRNAs based on miRNA-target interaction networks.

title:
  Genome-wide prediction and integrative functional characterization of Alzheimer’s disease-associated genes


id: 10_1101-2021_02_09_430363
title: Accommodating site variation in neuroimaging data using hierarchical and Bayesian models
author: Bayer, Johanna M. M.
date: 2021
txt: ./txt/10_1101-2021_02_09_430363.txt
cache: ./cache/10_1101-2021_02_09_430363.pdf
extension: .pdf
mime: application/pdf
summary:
  Accommodating site variation in neuroimaging data using hierarchical and Bayesian models The potential of normative modeling to make individualized predictions has led to structural neuroimaging results that go beyond the case-control approach. in a similar way for multi-site modeling in a pooled neuroimaging data set, which contained 7499 participants that org/abide/) data set to compare a non-linear, Gaussian version of the model, to a linear hierarchical Bayesian version and mathematical description of our approach to include site as predictor in a normative hierarchical Bayesian model. With the aim to create reliable normative models in multi-site neuroimaging data, we developed and compared two model is also able to capture non-linear effects between age and thickness of the cortical region ("Hierarchical Bayesian Gaussian Process term, which allows to model non-linear association between age and cortical thickness measures. The only models that perform better for most regions than the mean of the training data set are the Hierarchical Bayesian


id: 10_1101-2021_02_09_430460
title: Sequence neighborhoods enable reliable prediction of pathogenic mutations in cancer genomes
author: Banerjee, Shayantan
date: 2021
txt: ./txt/10_1101-2021_02_09_430460.txt
cache: ./cache/10_1101-2021_02_09_430460.pdf
extension: .pdf
mime: application/pdf
summary:
  experimentally validated cancer mutation data in this study, we explored various string-based evolutionary features resulted in the development of a pan-cancer mutation effect prediction Distinguishing between driver and passenger mutations from sequenced cancer genomes is a Recent studies have identified specific signatures or patterns of mutations in different cancer than passenger mutations and built probabilistic models to identify driver genes that had this study, missense mutations from 58 genes that were pan-cancer-based were combined from We used the same datasets to judge our model''s ability to predict rare driver mutations based Driver and Passenger Mutations'' Features Used to Train NBDriver are Significantly Although our method''s focus was to identify missense driver mutations from sequenced cancer surrounding driver and passenger mutations obtained from sequenced cancer genomes. computational prediction of driver missense mutations," Cancer Res., vol. functionally validated cancer-related missense mutations," Genome Biology, vol. Figure 7: Differences in the distribution of features between driver and passenger mutations 


id: 10_1101-2021_02_08_430070
title: On the application of BERT models for nanopore methylation detection
author: Zhang, Yao-zhong
date: 2021
txt: ./txt/10_1101-2021_02_08_430070.txt
cache: ./cache/10_1101-2021_02_08_430070.pdf
extension: .pdf
mime: application/pdf
summary:
  On the application of BERT models for nanopore methylation detection with deep learning models, have achieved significant performance improvements on nanopore methylation recurrent patterns of positional-signal-shift in the context window surrounding target 5-methylcytosine that the refined BERT model can achieve competitive or even better results than the state-of-the-art biRNN of datasets from the different research groups, BERT models demonstrate a good generalization Fig. 1: Basic BERT''s and refined BERT''s model structure used for methylation detection. a refined BERT model to take account of signal-shift patterns in the proposed refined BERT model achieves a competitive or even better result explore applying the BERT model for the nanopore methylation detection 2.2 Applying BERT models for nanopore methylation For the cross-sample evaluation, we train models on one dataset and test a BERT model to pay more attention to center positions. In-sample evaluation of different deep learning models on 5mC datasets.


id: 10_1101-2021_02_09_430036
title: A comparative study of genomic adaptations to low nitrogen availability in Genlisea aurea
author: Goldsborough, Thibaut
date: 2021
txt: ./txt/10_1101-2021_02_09_430036.txt
cache: ./cache/10_1101-2021_02_09_430036.pdf
extension: .pdf
mime: application/pdf
summary:
  A comparative study of genomic adaptations to low nitrogen availability in Genlisea aurea A comparative study of genomic adaptations to low nitrogen availability in Genlisea aurea is a carnivorous plant that grows on nitrogen-poor waterlogged sandstone aurea''s genome, CDS and non-coding DNA 2) Determination of transcriptomic nitrogen content and codon usage bias associated with higher nitrogen content tRNAs (among codons that are coding for the same amino a considerably lower number of nitrogen atoms in its genome than the two other plant species. has higher nitrogen counts per molecular unit in genomic DNA, CDS, Non-Coding DNA, protein, aurea has a higher nitrogen usage in its DNA, RNA and proteins Figure 2: Average number of nitrogen atoms per molecular unit in genomic DNA, CDS, Non-Coding DNA, aurea had lower nitrogen content in tRNA sequences but not in other Figure 3: Bar graph representing the codon usage bias and tRNA nitrogen content in G.


id: 10_1101-2021_02_08_428881
title: ACE: Explaining cluster from an adversarial perspective
author: Lu, Yang Young
date: 2021
txt: ./txt/10_1101-2021_02_08_428881.txt
cache: ./cache/10_1101-2021_02_08_428881.pdf
extension: .pdf
mime: application/pdf
summary:
  A common workflow in single-cell RNA-seq analysis is to project the data to a latent space, cluster the cells in that space, and identify sets of marker genes that explain the differences among the nonlinear embedding model which maps the gene expression to the low-dimensional representation where the groups A notable feature of ACE''s approach is that, by identifying genes jointly, the method moves away from the notion Input: gene expression matrix Deep autoencoder learns low-dimensional representation Embedding clustering Clustering is neuralized and concatenated with the encoder Differentiation analysis by ACE Output: gene relevance ACE takes as input a single-cell gene expression matrix and learns a low-dimensional representation for each Next, a neuralized version of the k-means algorithm is applied to the learned representation to identify cell groups. input gene expression profile that lead the neuralized clustering model to alter the assignment from one group to the other.


id: 10_1101-2021_02_08_430343
title: Patient-specific cell communication networks associate with disease progression in cancer
author: Gibbs, David L
date: 2021
txt: ./txt/10_1101-2021_02_08_430343.txt
cache: ./cache/10_1101-2021_02_08_430343.pdf
extension: .pdf
mime: application/pdf
summary:
  tumor microenvironment, the method identified ligands, receptors and cells meeting certain criteria of 56 9,234 samples in The Cancer Genome Atlas (TCGA), starting from a network of 64 cell types and 1,894 62 Data sources including TCGA and cell-sorted gene expression, bulk tumor expression, cell type scores, 78 ligands and receptors for each of the 64 cell types in xCell, using the source gene expression data. With this procedure, a network scaffold is induced, where cells produce ligands that bind to receptors on 113 (PFI) and tumor stage for each sample, a matrix of patient-specific edge weights was constructed 206 number of high weight edges in each tumor type did not associate with the number of samples, as might 254 in the tumor stage contrast, a majority of ligand-producing cells include GMP cells, Osteoblasts, MSC 283 In the PFI results, Th1 cells appeared in 13 high scoring edges in SKCM, all with 394 


id: 10_1101-2021_02_08_430275
title: Next-generation sequencing-based bulked segregant analysis without sequencing the parental genomes
author: Zhang, Jianbo
date: 2021
txt: ./txt/10_1101-2021_02_08_430275.txt
cache: ./cache/10_1101-2021_02_08_430275.pdf
extension: .pdf
mime: application/pdf
summary:
  Next-generation sequencing-based bulked segregant analysis without sequencing the parental genomes identified using BSA-Seq, a technology in which next-generation sequencing (NGS) is applied to bulked segregant analysis (BSA). recently developed the significant structural variant method for BSASeq data analysis that exhibits higher detection power than standard to analyze BSA-Seq data in which genome sequences of one parent served as the reference sequences in genotype calling, and thus We analyzed a public BSA-Seq dataset using our modified method and the standard allele frequency and Gmethod allows the detection of such associations without sequencing the parental genomes, leading to further lower the the BSA-Seq data with the genome sequences of both the parents101 when the parental genome sequences are used to aid BSA-Seq data 193 The allele frequency method: The ΔAF value of each SNP in 267 BSA-Seq data analysis using the genome sequences of both the parents and the bulks. BSA-Seq data analysis using only the bulk genome sequences.


id: 10_1101-2021_02_08_430270
title: Scalable Bias-corrected Linkage Disequilibrium Estimation Under Genotype Uncertainty
author: Gerard, David
date: 2021
txt: ./txt/10_1101-2021_02_08_430270.txt
cache: ./cache/10_1101-2021_02_08_430270.pdf
extension: .pdf
mime: application/pdf
summary:
  Scalable Bias-corrected Linkage Disequilibrium Estimation Under Genotype Uncertainty Keywords and phrases: attenuation bias, genotype likelihood, linkage disequilibrium, polyploidy, reliability ratio. Let XiA and XiB be the posterior means at loci A and B for individual Equations (5)–(7) take the naive estimators most researchers use in practice (the sample covariance/correlation of posterior means) and inflate these by a multiplicative effect. Gerard and Ferrão, 2019] to obtain the posterior moments for each individual''s genotype at each SNP reliability ratios of most SNPs only increase their correlation estimates by less than 10%. To evaluate the LD estimates of high reliability ratio SNPs, we calculated the MLEs for ρ2 applied to simple linear regression with an additive effects model (where the SNP effect is proportional to the dosage), result in the standard ordinary least squares estimates when using the extreme reliability ratio of PotVar0080327, the genotype-error adjusted correlation estimate is -1.


id: 10_1101-2021_02_10_430604
author: Youngblut, Nicholas D.
date: 2021
txt: ./txt/10_1101-2021_02_10_430604.txt
cache: ./cache/10_1101-2021_02_10_430604.pdf
extension: .pdf
mime: application/pdf
summary:
  Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets 1 Struo2: efficient metagenome profiling database construction for ever-expanding 10 Mapping metagenome reads to reference databases is the standard approach for 12 reference databases often lack recently generated genomic data such as 15 method for constructing custom databases; however, the pipeline does not scale well with the 17 not allow for efficient database updating as new data are generated. 20 HUMAnN3 databases that can be easily updated with new genomes and/or individual gene Struo2 enables feasible database generation for continually increasing large-scale 25 ● Pre-built databases: http://ftp.tue.mpg.de/ebio/projects/struo2/ 26 ● Utility tools: https://github.com/nick-youngblut/gtdb_to_taxdump 28 Metagenome profiling involves mapping reads to reference sequence databases and is 39 computational resources, which led us to create Struo for straight-forward custom metagenome 54 CPU hours per genome versus ~2.4 for Struo (Figure 1B). 67 taxonomy (available at https://github.com/nick-youngblut/gtdb_to_taxdump ). (2020) Struo: a pipeline for building custom databases for 

title:
  Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets


id: 10_1101-2021_02_08_430280
author: Kasukurthi, Mohan V
date: 2021
txt: ./txt/10_1101-2021_02_08_430280.txt
cache: ./cache/10_1101-2021_02_08_430280.pdf
extension: .pdf
mime: application/pdf
summary:
  given transcriptome provided as either a raw user-generated RNA-Seq dataset or NCBI SRR file identifier. SURFR identifies all ncRNA fragments (both annotated and novel) and their expressions in up to ten datasets per comprehensively compare all fragment expressions identified in up to 30 individual datasets by entering multiple SURFR session IDs window detailing each fragment identified in the individual, selected small RNA-Seq dataset. of the results page redirects the user to a SURFR window detailing the expressions of all full length sncRNAs in the provided datasets. Fragments" window (Figure 2D) for each fragment identified in the individual, selected small RNA-Seq dataset within its host gene along with the fragment''s expression (RPM) in each individual small RNA-Seq dataset, and lncRNAs expressed in a given human transcriptome from either a user-provided RNA-Seq dataset or publically More importantly, however, LAGOOn identified MALAT1 as the most highly expressed lncRNA in MDAMB-231 breast cancer cells (Figure 9).

title:
  SALTS – <span class="underline">S</span>URFR (sncRNA) <span class="underline">A</span>nd <span class="underline">L</span>AGOOn (lncRNA) <span class="underline">T</span>ranscriptomics <span class="underline">S</span>uite


id: 10_1101-2020_09_02_279521
title: Simulating the outcome of amyloid treatments in Alzheimer’s disease from imaging and clinical data
author: Abi Nader, Clément
date: 2021
txt: ./txt/10_1101-2020_09_02_279521.txt
cache: ./cache/10_1101-2020_09_02_279521.pdf
extension: .pdf
mime: application/pdf
summary:
  Simulating the outcome of amyloid treatments in Alzheimer''s disease from imaging and clinical data When applied to multimodal imaging and clinical data from the Alzheimer''s Disease Neuroimaging Initiative our * Data used in preparation of this article were obtained from the Alzheimer''s Disease Neuroimaging Initiative (ADNI) database Keywords : Alzheimer''s Disease ; Clinical trials ; Disease progression; Amyloid hypothesis; of large datasets of different data modalities, such as clinical scores, or brain imaging measures to model Alzheimer''s disease progression based on specific assumptions on the biochemical combining traditional DPMs with dynamical models of Alzheimer''s disease progression. In this work we present a novel computational model of Alzheimer''s disease progression to multi-modal imaging and clinical data from the Alzheimer''s Disease Neuroimaging To simulate the long-term progression of Alzheimer''s disease we first project the AD subjects Figure 3 Model-based progression of Alzheimer''s disease. clinical data, based on the estimation of latent biomarkers'' relationships governing Alzheimer''s 


id: 10_1101-2020_02_04_934216
title: EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning
author: Kirchoff, Kathryn E.
date: 2021
txt: ./txt/10_1101-2020_02_04_934216.txt
cache: ./cache/10_1101-2020_02_04_934216.pdf
extension: .pdf
mime: application/pdf
summary:
  EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning task of kinase-motif phosphorylation prediction as a multi-label kinase or substrate, as well as protein scaffolds that facilitate structural orientation and downstream catalysis of the reaction, modify the efficacy of motif phosphorylation. prediction of phosphorylation events), a deep learning approach for predicting multi-label kinase-motif phosphorylation relationships. example, the TLK kinase family only has nine positive labels (verified TLK-motif interactions) and more than 10,000 resulting data set is comprised of 7302 phosphorylatable motifs and their reaction-associated kinase families (Table 1). The final output is a vector, k, of length eight, where each value corresponds to the probability that the motif a was phosphorylated by one of the kinase families indicated in We sought to illuminate the relationship between kinase-family dissimilarity and phosphorylated motif-group dissimilarity described results provide motivation to incorporate both motif dissimilarity and kinase relatedness into the predictive model, as of kinase-motif prediction compared to the single-label approaches.


