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PLOS Computational Biology is a peer-reviewed journal published by PLOS, covering Neural dynamics and brain function, Bioinformatics and Genomic Networks, Gene Regulatory Network Analysis. Over its lifetime it has published 12,902 papers, which have received 683,633 citations.
| Publisher | PLOS |
|---|---|
| Reference style | Numbered (Vancouver) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C. A representative article title. PLOS Computational Biology. 2023;12(3):45–58.
Formats any DOI in PLOS Computational Biology style. No sign-up. |
| Publishes research in | Neural dynamics and brain function Bioinformatics and Genomic Networks Gene Regulatory Network Analysis Protein Structure and Dynamics Genomics and Phylogenetic Studies |
| ISSN | 1553-734X |
| Citation impact (2-yr) | 4.08 |
| h-index | 288 |
| i10-index | 8,746 |
| Article processing charge | $2,655 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | journals.plos.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from…
Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm.…
We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2…
We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support…
Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates,…
