Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Cheng-Zhong Zhang

dblp:182/4796 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequencing data analysis
read filtering
0.212016
VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016
Bioinformatics and computational biology › bioinformatics infrastructure
sequencing data compression
0.212016
VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016
Bioinformatics and computational biology › sequence analysis
sequencing data processing
0.212016
VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules · Bioinform. 2016

Methods — techniques the papers use, named apart from their topics

sequence motif matching · 0.2region-specific filtering rules · 0.2
YearPublicationVenuePosition
2016 VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules
abstract
UNLABELLED: We developed VariantBam, a C ++ read filtering and profiling tool for use with BAM, CRAM and SAM sequencing files. VariantBam provides a flexible framework for extracting sequencing reads or read-pairs that satisfy combinations of rules, defined by any number of genomic intervals or variant sites. We have implemented filters based on alignment data, sequence motifs, regional coverage and base quality. For example, VariantBam achieved a median size reduction ratio of 3.1:1 when applied to 10 lung cancer whole genome BAMs by removing large tags and selecting for only high-quality variant-supporting reads and reads matching a large dictionary of sequence motifs. Thus VariantBam enables efficient storage of sequencing data while preserving the most relevant information for downstream analysis. AVAILABILITY AND IMPLEMENTATION: VariantBam and full documentation are available at github.com/jwalabroad/VariantBam CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Jeremiah A. Wala, Cheng-Zhong Zhang, Matthew Meyerson, Rameen Beroukhim
Bioinform.2