scispace - formally typeset
M

Matthew D. MacManes

Researcher at University of New Hampshire

Publications -  88
Citations -  9629

Matthew D. MacManes is an academic researcher from University of New Hampshire. The author has contributed to research in topics: Peromyscus & Cactus mouse. The author has an hindex of 21, co-authored 84 publications receiving 7829 citations. Previous affiliations of Matthew D. MacManes include California Institute for Quantitative Biosciences & University of California, Berkeley.

Papers
More filters
Journal ArticleDOI

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.
Journal ArticleDOI

Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Keith Bradnam, +95 more
- 23 Jan 2013 - 
TL;DR: The Assemblathon 2 as mentioned in this paper presented a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and a snake) from 21 participating teams.
Journal ArticleDOI

Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Keith Bradnam, +98 more
- 22 Jul 2013 - 
TL;DR: The Assemblathon 2 as discussed by the authors presented a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and a snake) from 21 participating teams.

De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity

TL;DR: This protocol describes the use of the Trinity platform for de novo transcriptome assembly from RNA-Seq data in non-model organisms and presents Trinity’s supported companion utilities for downstream applications, including RSEM for transcript abundance estimation and R/Bioconductor packages for identifying differentially expressed transcripts across samples.
Journal ArticleDOI

On the optimal trimming of high-throughput mRNA sequence data

TL;DR: Although very aggressive quality trimming is common, this study suggests that a more gentle trimming, specifically of those nucleotides whose Phred score <2 or <5, is optimal for most studies across a wide variety of metrics.