Bioclojure – Functional Library for the Manipulation of Biological Sequences

Bioclojure

:: DESCRIPTION

BioClojure is an open-source library for the manipulation of biological sequence data written in the language Clojure. BioClojure aims to provide a functional framework for the processing of biological sequence data that provides simple mechanisms for concurrency and lazy evaluation of large datasets.

::DEVELOPER

Bioclojure team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
Java

:: DOWNLOAD

BioClojure

:: MORE INFORMATION

Citation

Bioclojure: a functional library for the manipulation of biological sequences.
Plieskatt J, Rinaldi G, Brindley PJ, Jia X, Potriquet J, Bethony J, Mulvenna J.
Bioinformatics. 2014 May 2. pii: btu311

BioSeq-Analysis 2.0 / BioSeq-BLM 1.0 – Analyzing DNA, RNA, and Protein Sequences based on Biological Language Models

BioSeq-Analysis 2.0 / BioSeq-BLM 1.0

:: DESCRIPTION

BioSeq-Analysis is an platform for analyzing DNA, RNA, and protein sequences at sequence level and residue level based on machine learning approaches

BioSeq-BLM is a platform for analyzing DNA, RNA, and protein sequences based on biological language models

::DEVELOPER

Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux

:: DOWNLOAD

BioSeq-Analysis / BioSeq-BLM

:: MORE INFORMATION

Citation

Li HL, Pang YH, Liu B.
BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models.
Nucleic Acids Res. 2021 Sep 28:gkab829. doi: 10.1093/nar/gkab829. Epub ahead of print. PMID: 34581805.

Liu B, Gao X, Zhang H.
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.
Nucleic Acids Res. 2019 Nov 18;47(20):e127. doi: 10.1093/nar/gkz740. PMID: 31504851; PMCID: PMC6847461.

Parallel-META 3.6 – A Parallel Metagenomic Analysis Pipeline

Parallel-META 3.6

:: DESCRIPTION

Parallel-META is a GPGPU and Multi-Core CPU based software which can parallelly analyze massive metagenomic data structures, report the classification, construction and distribution on phylogenetic & taxonomic and functional level.

::DEVELOPER

Bioinformatics Group , Single-cell Reseearch Center of Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences (QIBEBT-CAS).

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOs
C Compiler
R

:: DOWNLOAD

Parallel-META

:: MORE INFORMATION

Citation

Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization.
Su X, Pan W, Song B, Xu J, Ning K.
PLoS One. 2014 Mar 3;9(3):e89323. doi: 10.1371/journal.pone.0089323.

Parallel-META: efficient metagenomic data analysis based on high-performance computation.
Su X, Xu J, Ning K.
BMC Syst Biol. 2012 Jul 16;6 Suppl 1:S16. doi: 10.1186/1752-0509-6-S1-S16.

Genoppi 1.0.13 – Integration of Proteomic and Genetic data

Genoppi 1.0.13

:: DESCRIPTION

Genoppi is an open-source software for performing quality control and analyzing quantitative proteomic data. Genoppi streamlines the integration of proteomic data with external datasets such as known protein-protein interactions in published literature, data from genetic studies, gene set annotations, or other user-defined inputs.

::DEVELOPER

Lage Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
R

:: DOWNLOAD

Genoppi

:: MORE INFORMATION

Citation

Pintacuda G, Lassen FH, Hsu YH, Kim A, Martín JM, Malolepsza E, Lim JK, Fornelos N, Eggan KC, Lage K.
Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data.
Nat Commun. 2021 May 10;12(1):2580. doi: 10.1038/s41467-021-22648-5. PMID: 33972534; PMCID: PMC8110583.

Pse-Analysis 1.0 – Sequence analysis based on Pseudo Components and Kernel methods

Pse-Analysis 1.0

:: DESCRIPTION

Pse-Analysis is a Python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods.

::DEVELOPER

Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux
Python

:: DOWNLOAD

Pse-Analysis

:: MORE INFORMATION

Citation

Liu B, Wu H, Zhang D, Wang X, Chou KC.
Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.
Oncotarget. 2017 Feb 21;8(8):13338-13343. doi: 10.18632/oncotarget.14524. PMID: 28076851; PMCID: PMC5355101.

PiGx v0.0.3 – Pipelines in Genomics

PiGx v0.0.3

:: DESCRIPTION

PiGx is a collection of genomics pipelines. All pipelines are easily configured with a simple sample sheet and a descriptive settings file. The result is a set of comprehensive, interactive HTML reports with interesting findings about your samples.

::DEVELOPER

Bioinformatics & Omics Data Science platform

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

PiGx

:: MORE INFORMATION

Citation

Wurmus R, Uyar B, Osberg B, Franke V, Gosdschan A, Wreczycka K, Ronen J, Akalin A.
PiGx: reproducible genomics analysis pipelines with GNU Guix.
Gigascience. 2018 Dec 1;7(12):giy123. doi: 10.1093/gigascience/giy123. PMID: 30277498; PMCID: PMC6275446.

WebMGA – Web Server for fast Metagenomic Sequence Analysis

WebMGA

:: DESCRIPTION

WebMGA is a customizable web server for fast metagenomic analysis. WebMGA includes over 20 commonly used tools such as ORF calling, sequence clustering, quality control of raw reads, removal of sequencing artifacts and contaminations, taxonomic analysis, functional annotation etc.

::DEVELOPER

Group of Weizhong Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

BMC Genomics. 2011 Sep 7;12:444. doi: 10.1186/1471-2164-12-444.
WebMGA: a customizable web server for fast metagenomic sequence analysis.
Wu S1, Zhu Z, Fu L, Niu B, Li W.

GC4S v1.6.0 – Bioinformatics-oriented collection of GUI Components for (Java) Swing

GC4S v1.6.0

:: DESCRIPTION

GC4S is an open-source library that provides a bioinformatics-oriented collection of GUI Components for (Java) Swing.

::DEVELOPER

SING Group.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / WIndows
Java

:: DOWNLOAD

GC4S

:: MORE INFORMATION

Citation

López-Fernández H, Reboiro-Jato M, Glez-Peña D, Laza R, Pavón R, Fdez-Riverola F.
GC4S: A bioinformatics-oriented Java software library of reusable graphical user interface components.
PLoS One. 2018 Sep 20;13(9):e0204474. doi: 10.1371/journal.pone.0204474. PMID: 30235322; PMCID: PMC6147514.

deepTools 3.5.1 – Tools for Exploring Deep Sequencing data

deepTools 3.5.1

:: DESCRIPTION

deepTools is a suite of python tools particularly developed for the efficient analysis of high-throughput sequencing data, such as ChIP-seq, RNA-seq or MNase-seq.

::DEVELOPER

he Bioinformatics Facility at the Max Planck Institute for Immunobiology and Epigenetics, Freiburg.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/ Linux / MacOsX
Python

:: DOWNLOAD

deepTools

:: MORE INFORMATION

Citation:

Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T.
deepTools2: a next generation web server for deep-sequencing data analysis.
Nucleic Acids Res. 2016 Jul 8;44(W1):W160-5. doi: 10.1093/nar/gkw257. Epub 2016 Apr 13. PMID: 27079975; PMCID: PMC4987876.

Ramírez F, Dündar F, Diehl S, Grüning BA, Manke T.
deepTools: a flexible platform for exploring deep-sequencing data.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W187-91. doi: 10.1093/nar/gku365. Epub 2014 May 5. PMID: 24799436; PMCID: PMC4086134.

BioJS 2.0 – A library of JavaScript Components to Represent Biological Data

BioJS 2.0

:: DESCRIPTION

BioJS is an open-source project whose main objective is the visualization of biological data in JavaScript. BioJS provides an easy-to-use consistent framework for bioinformatics application programmers.

::DEVELOPER

BioJS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
Java

:: DOWNLOAD

BioJS

:: MORE INFORMATION

Citation

BioJS: an open source JavaScript framework for biological data visualization.
Gómez J, García LJ, Salazar GA, Villaveces J, Gore S, García A, Martín MJ, Launay G, Alcántara R, Del-Toro N, Dumousseau M, Orchard S, Velankar S, Hermjakob H, Zong C, Ping P, Corpas M, Jiménez RC.
Bioinformatics. 2013 Apr 15;29(8):1103-4.