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Long-read sequencing transcriptome quantification with lr-kallisto

delete2025-12-01
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PRE
AI
R
Rebekah K. Loving
D
Delaney K. Sullivan
F
Fairlie Reese
E
Elisabeth Rebboah
J
Jasmine Sakr
N
Narges Rezaie
H
Heidi Yahan Liang
G
Ghassan Filimban
S
Shimako Kawauchi
A
A. Sina Booeshaghi
P
Páll Melsted
C
Conrad Oakes
D
Diane Trout
B
Brian A. Williams
G
Grant R. MacGregor
B
B Wold
A
A Mortazavi *
L
Lior Pachter *
DOI:10.1371/journal.pcbi.1013692delete
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摘要

摘要

En 中文
RNA丰度定量因高通量短读长技术而变得常规且经济实惠,这些技术能在基因水平提供准确的分子计数。同样地,尽管全长的、明确的转录本异构体的定量具有广泛的生物学意义,但其准确且经济的定量仍是一个棘手的挑战。长读长测序平台现在产生的数据类型原则上可以推动明确的异构体定量常规化。然而,当代长读长数据类型的某些特性,连同异构体复杂性和遗传变异,带来了生物信息学上的挑战。本文使用ONT数据表明,长读长数据的快速准确定量是可能的,并且通过外显子捕获可得到改进。为执行定量分析,我们开发了lr-kallisto,该工具将kallisto的批量和小规模单细胞RNA-seq定量方法适配于长读长技术。
Keyword:
long-read sequencing
transcriptome quantification
lr-kallisto
RNA-seq
isoform quantification

期刊

P
PLoS Computational Biology
IF:
3.6
论文数:
643
被引数:
0

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David Geffen School of Medicine at UCLA
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university of california los angeles
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C
california institute of technology
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University of California System
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