BOL: Related items

Porechop: tool for finding and removing adapters from Oxford Nanopore reads

Rahul Nayak — Tue, 29 May 2018 07:33:44 -0500

Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.

Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

Address of the bookmark: https://github.com/rrwick/Porechop

HiGlass: a tool for exploring genomic contact matrices and tracks.

Jit — Mon, 11 Jun 2018 09:44:49 -0500

HiGlass is a tool for exploring genomic contact matrices and tracks. Please take a look at the examples and documentation for a description of the ways that it can be configured to explore and compare contact matrices. To load private data, HiGlass can be run locally within a Docker container. The HiC data in the examples below is from Rao et al. (2014) http://higlass.io/

Address of the bookmark: http://higlass.io/

AlignQC: A tool for assessing an alignment, and generating reports that are easy to share

Jit — Tue, 07 Aug 2018 04:41:07 -0500

Long read alignment analysis. Generate a reports on sequence alignments for mappability vs read sizes, error patterns, annotations and rarefraction curve analysis. The most basic analysis only requires a BAM file, and outputs a web browser compatible xhtml to visualize/share/store/extract analysis results.

https://f1000research.com/articles/6-100/

https://github.com/jason-weirather/AlignQC

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/AlignQC/

LoRMA: A tool for correcting sequencing errors in long reads

Abhimanyu Singh — Thu, 06 Sep 2018 16:21:01 -0500

An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of k-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75×, the throughput of the new method is at least 20% higher.

conda install -c atgc-montpellier lorma

Address of the bookmark: https://gite.lirmm.fr/lorma/lorma-releases/wikis/home

ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information

Rahul Nayak — Thu, 04 Oct 2018 16:39:28 -0500

This is a tool for detecting structural variations using soft-clipping information From SAM files.

https://github.com/shinout/clipcrop

Address of the bookmark: https://github.com/shinout/clipcrop

Patterns: a modeling tool dedicated to biological network modeling

Abhimanyu Singh — Fri, 26 Jul 2019 01:11:59 -0500

It is designed to work with patterned data. Famous examples of problems related to patterned data are:

recovering signals in networks after a stimulation (cascade network reverse engineering),
analysing periodic signals.

Address of the bookmark: https://github.com/fbertran/Patterns

iSeqQC: a tool for expression-based quality control in RNA sequencing

BioStar — Sun, 16 Feb 2020 08:47:17 -0600

iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers.

http://cancerwebpa.jefferson.edu/iSeqQC/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8

Address of the bookmark: https://github.com/gkumar09/iSeqQC

WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results

BioStar — Sun, 12 Apr 2020 10:02:22 -0500

WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we’ve made:
1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.
2. We have added the reference data of 9 species for users selection.
3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.

Address of the bookmark: http://wego.genomics.org.cn/

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

MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data

BioStar — Thu, 26 Nov 2020 11:05:04 -0600

The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings:

1) A cell line expressing or lacking single TF,

2) Breast tumors divided along PAM50 designations

3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype

4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels.

In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.

More at https://uwmadison.app.box.com/s/8j90e5h2rjrsz3bacaxnq8kor2o64vyg

Address of the bookmark: https://github.com/asroopra/MAGIC