Author

Pengyao Ping

University of Technology SydneyORCID 0000-0002-1829-3273Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Jinyan LiiD· 2 shared Shuquan SuiD· 2 shared Tian LaniD· 2 shared Hui PengiD· 1 shared Wei LiuiD· 1 shared Wei LiuiD· 1 shared Xinhui CaiiD· 1 shared Xuan ZhangiD· 1 shared

Activity by Year

2024-2024 · peak 2/yr

Co-Author Collaboration Network

9 top collaborators · 31 mutual co-author links

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2 shared papersBioinformatics

Jinyan LiORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 30, 2024

How Error Correction Affects PCR Deduplication: A Survey Based on UMI Datasets of Short Reads

Pengyao Ping, Tian Lan, Shuquan Su…·Jinyan Li

This paper surveys PCR-deduplication and error-correction methods on short reads, finding that computational approaches differ significantly from UMI-based methods and leave many errors uncorrected.

DOI 10.1101/2024.05.30.596723

bulk RNA-seqstatistical model

𝕏 PostBluesky

BioinformaticsbioRxivPreprintApr 5, 2024

Turn `noise' to signal: accurately rectify millions of erroneous short reads through graph learning on edit distances

Pengyao Ping, Shuquan Su, Xinhui Cai…·Jinyan Li

This paper introduces a method to correct millions of erroneous short reads by leveraging graph learning on edit distances, improving downstream applications like genome abundance quantification and SNP profiling.

1 citation

bulk RNA-seqmachine learning (classical)

𝕏 PostBluesky