BOL: BioStar's bookmarks

RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

BioStar — Sun, 20 Sep 2020 06:03:42 -0500

RosettaAntibodyDesign (RAbD) is a generalized framework for the design of antibodies, in which a user can easily tailor the run to their project needs. The algorithm is meant to sample the diverse sequence, structure, and binding space of an antibody-antigen complex. It can be used for a multitude of project types, from denovo design to redesigns that improve binding affinity, optimize stability, or manipulate function.

The framework is based on rigorous bioinformatic analysis and rooted very much on our recent clustering of antibody CDR regions. It uses the North/Dunbrack CDR definition as outlined in the North/Dunbrack clustering paper.

More at

https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign

https://bio-jade.readthedocs.io/en/latest/installation.html

Address of the bookmark: https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign

Clustergrammer is a web-based tool for visualizing high-dimensional data as an interactive and shareable hierarchically clustered heatmap

BioStar — Sun, 23 Aug 2020 19:30:17 -0500

Clustergrammer is a web-based tool for visualizing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap. Clustergrammer's front end (Clustergrammer-JS) is built using D3.js and its back-end (Clustergrammer-PY) is built using Python. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several biology-specific features (e.g. enrichment analysis, see Biology-Specific Features) to facilitate the exploration of gene-level biological data.

Address of the bookmark: https://github.com/MaayanLab/clustergrammer

parallelLastz: Lastz with multi-threads support.

BioStar — Sat, 22 Aug 2020 05:58:40 -0500

Running Lastz (https://github.com/lastz/lastz) in parallel mode. This program is for single computer with multiple core processors.

When the query file format is fasta, you can specify many threads to process it. It can reduce run time linearly, and use almost equal memory as the original lastz program. This is useful when you lastz a big query file to a huge reference like human whole genome sequence.

The program is an extension on the original lastz program which was written by Bob Harris (the LASTZ guy).

Address of the bookmark: https://github.com/jnarayan81/parallelLastz

chromatiblock: Scalable, whole-genome visualisation of structural changes in prokaryotes

BioStar — Sat, 22 Aug 2020 05:17:18 -0500

To create a fresh environment for chromatiblock to run in do:

conda create --name chromatiblock
conda activate chromatiblock
conda install chromatiblock --channel conda-forge --channel bioconda

Then in future to run chromatiblock you can reactivate this environemtn using conda activate chromatiblock

Direct download:

Alternatively you can download and run the script from here.

Address of the bookmark: https://github.com/mjsull/chromatiblock

Sibelia: A comparative genomics tool

BioStar — Sat, 22 Aug 2020 02:49:00 -0500

Sibelia: A comparative genomics tool: It assists biologists in analysing the genomic variations that correlate with pathogens, or the genomic changes that help microorganisms adapt in different environments. Sibelia will also be helpful for the evolutionary and genome rearrangement studies for multiple strains of microorganisms.

Sibelia is useful in finding: (1) shared regions, (2) regions that present in one group of genomes but not in others, (3) rearrangements that transform one genome to other genomes.

More at http://bioinf.spbau.ru/sibelia

Sibelia docs http://gensoft.pasteur.fr/docs/Sibelia/3.0.7/SIBELIA.md

Address of the bookmark: https://github.com/bioinf/Sibelia

mixtureS: a novel tool for bacterial strain reconstruction from reads

BioStar — Fri, 21 Aug 2020 08:23:19 -0500

mixtureS that can de novo identify bacterial strains from shotgun reads of a clonal or metagenomic sample, without prior knowledge about the strains and their variations. Tested on 243 simulated datasets and 195 experimental datasets, mixtureS reliably identified the strains, their numbers and their abundance. Compared with three tools, mixtureS showed better performance in almost all simulated datasets and the vast majority of experimental datasets.

Availability

The source code and tool mixtureS is available at http://www.cs.ucf.edu/˜xiaoman/mixtureS/.

Address of the bookmark: http://www.cs.ucf.edu/~xiaoman/mixtureS/

SqueezeMeta: a fully automated metagenomics pipeline, from reads to bins

BioStar — Mon, 17 Aug 2020 05:25:10 -0500

SqueezeMeta is a full automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual genomes via binning procedures. Thus, SqueezeMeta features several unique characteristics:

Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
Co-assembly of a large number of metagenomes via merging of individual metagenomes
Includes binning and bin checking, for retrieving individual genomes
The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

BioStar — Mon, 17 Aug 2020 05:21:09 -0500

SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).
The bioinformatics treatment is based on Vsearch [Rognes 2016] which showed to be both accurate and fast [Wescott 2015].The statistical analysis is based on DESeq2 R package [Anders and Huber 2010] which robustly identifies the differential abundant features as suggested in [McMurdie and Holmes 2014] and [Jonsson2016]. SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [Love 2014].
SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available here and a poster here.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4

Address of the bookmark: https://github.com/aghozlane/shaman

ClueGO: a Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes

BioStar — Thu, 13 Aug 2020 10:24:29 -0500

ClueGO is a Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network and it can be used in combination with GOlorize. The identifiers can be uploaded from a text file or interactively from a network of Cytoscape. The type of identifiers supported can be easely extended by the user. ClueGO performs single cluster analysis and comparison of clusters. From the ontology sources used, the terms are selected by different filter criteria. The related terms which share similar associated genes can be fused to reduce redundancy. The ClueGO network is created with kappa statistics and reflects the relationships between the terms based on the similarity of their associated genes. On the network, the node colour can be switched between functional groups and clusters distribution. ClueGO charts are underlying the specificity and the common aspects of the biological role. The significance of the terms and groups is automatically calculated. ClueGO is easy updatable with the newest files from Gene Ontology and KEGG.

Address of the bookmark: http://www.ici.upmc.fr/cluego/

proActiv: Estimation of Promoter Activity from RNA-Seq data

BioStar — Thu, 13 Aug 2020 10:21:44 -0500

proActiv is an R package that estimates promoter activity from RNA-Seq data. proActiv uses aligned reads and genome annotations as input, and provides absolute and relative promoter activity as output. The package can be used to identify active promoters and alternative promoters, the details of the method are described in Demircioglu et al.

Additional data on differential promoters in tissues and cancers from TCGA, ICGC, GTEx, and PCAWG can be downloaded here: https://jglab.org/data-and-software/

Address of the bookmark: https://github.com/GoekeLab/proActiv