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20112013
most citedScaling metagenome sequence assembly with probabilistic de Bruijn graphs

261 citations · 635 across the 5 of their papers we have counts for

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5 papers · 1 filter

q-bio.GN2013★ 120 cited

These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure

Qingpeng Zhang, Jason Pell, Rosangela Canino-Koning +2

K-mer abundance analysis is widely used for many purposes in nucleotide sequence analysis, including data preprocessing for de novo assembly, repeat detection, and sequencing cover…

q-bio.GN2012★ 7 cited

Assembling large, complex environmental metagenomes

Adina Chuang Howe, Janet Jansson, Stephanie A. Malfatti +3

The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively re…

q-bio.GN2012★ 7 cited

Illumina Sequencing Artifacts Revealed by Connectivity Analysis of Metagenomic Datasets

Adina Chuang Howe, Jason Pell, Rosangela Canino-Koning +5

Sequencing errors and biases in metagenomic datasets affect coverage-based assemblies and are often ignored during analysis. Here, we analyze read connectivity in metagenomes and i…

q-bio.GN2012★ 240 cited

A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data

C. Titus Brown, Adina Howe, Qingpeng Zhang +2

Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified single-cell genomes, and metagenomes has enabled investigation of a wide range of organisms and ecosystem…

q-bio.GN2011★ 261 cited

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs

Jason Pell, Arend Hintze, Rosangela Canino-Koning +3

Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for {\em de novo} assembly of short-read shotg…