BOL: Related items

HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies

Jit — Tue, 15 May 2018 07:35:26 -0500

HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to: NGS short reads (Illumina HiSeq) clone-based sequencing (Fosmid or BAC clones) SMRT reads (PacBio) Oxford Nanopore reads 10X Genomics Linked-Reads proximity-ligation (Hi-C) reads high-coverage sequencing (>40x coverage-per-SNP) using above technologies combinations of the above technologies (e.g. scaffold long reads with Hi-C reads) See below for specific examples of command line options and best practices for some of these technologies. NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants. If you use HapCUT2 in your research, please cite: Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116

Address of the bookmark: https://github.com/vibansal/HapCUT2

Artificial intelligence is more accurate than doctors in diagnosing breast cancer

BioStar — Wed, 01 Jan 2020 22:12:34 -0600

Artificial intelligence is more accurate than doctors in diagnosing breast cancer from mammograms, a study in the journal Nature suggests.

An international team, including researchers from Google Health and Imperial College London, designed and trained a computer model on X-ray images from nearly 29,000 women.

The algorithm outperformed six radiologists in reading mammograms.

AI was still as good as two doctors working together.

Unlike humans, AI is tireless. Experts say it could improve detection. Read More: https://www.bbc.com/news/health-50857759

HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads

BioStar — Fri, 27 Mar 2020 22:49:31 -0500

HiCanu, a significant modification of the Canu assembler designed to leverage the full potential of HiFi reads via homopolymer compression, overlap-based error correction, and aggressive false overlap filtering.

More at https://www.biorxiv.org/content/10.1101/2020.03.14.992248v3

Address of the bookmark: https://github.com/marbl/canu

Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm

BioStar — Mon, 16 Mar 2020 10:09:26 -0500

Apollo is an assembly polishing algorithm that attempts to correct the errors in an assembly. It can take multiple set of reads in a single run and polish the assemblies of genomes of any size. Described by Firtina et al. (preliminary version at https://arxiv.org/pdf/1902.04341.pdf

More at https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa179/5804978?rss=1

Address of the bookmark: https://github.com/CMU-SAFARI/Apollo

Grinder / Biogrinder - A versatile omics shotgun and amplicon sequencing read simulator

Jit — Wed, 24 May 2017 08:41:41 -0500

Grinder is a versatile program to create random shotgun and amplicon sequence libraries based on DNA, RNA or proteic reference sequences provided in a FASTA file.

Grinder can produce genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, metaproteomic shotgun and amplicon datasets from current sequencing technologies such as Sanger, 454, Illumina. These simulated datasets can be used to test the accuracy of bioinformatic tools under specific hypothesis, e.g. with or without sequencing errors, or with low or high community diversity. Grinder may also be used to help decide between alternative sequencing methods for a sequence-based project, e.g. should the library be paired-end or not, how many reads should be sequenced.

Address of the bookmark: https://sourceforge.net/projects/biogrinder/files/biogrinder/

RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data

Jit — Mon, 27 Nov 2017 08:25:33 -0600

RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.

This work has been accepted for publication:

MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to Nucleic Acids Research April 4, 2012.

If you have any questions or bug reports, please let us know at .

Address of the bookmark: http://kiwi.cs.dal.ca/Software/RITA

BioCircos.js is an open source interactive Javascript library to interactive display biological data on the web

Jit — Fri, 19 Jan 2018 15:03:51 -0600

BioCircos.js is an open source interactive Javascript library which provides an easy way to interactive display biological data on the web. It implements a raster-based SVG visualization using the open source Javascript framework jquery.js. BioCircos.js is multiplatform and works in all major internet browsers (Internet Explorer, Mozilla Firefox, Google Chrome, Safari, Opera). Its speed is determined by the client’s hardware and internet browser. For smoothest user experience, we recommend Google Chrome.

BioCircos.js provides SNP, CNV, HEATMAP, LINK, LINE, SCATTER, ARC, TEXT, and HISTGRAMmodules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides BACKGROUND module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.

Demo

Address of the bookmark: http://bioinfo.ibp.ac.cn/biocircos/document/index.html

Epiviz: an interactive visualization tool for functional genomics data.

Jit — Mon, 09 Jul 2018 05:27:39 -0500

Epiviz is an interactive visualization tool for functional genomics data. It supports genome navigation like other genome browsers, but allows multiple visualizations of data within genomic regions using scatterplots, heatmaps and other user-supplied visualizations. It also includes data from the Gene Expression Barcode project for transcriptome visualization. It has a flexible plugin framework so users can addd3 visualizations. You can see a video tour here.

https://bioconductor.org/packages/release/bioc/html/epivizr.html

https://github.com/epiviz

https://github.com/epiviz/epiviz

Address of the bookmark: https://epiviz.github.io/

VariantBam: Filtering and profiling of next-generational sequencing data using region-specific rules

Rahul Nayak — Thu, 04 Oct 2018 16:30:44 -0500

VariantBam is a tool to extract/count specific sets of sequencing reads from next-generational sequencing files. To save money, disk space and I/O, one may not want to store an entire BAM on disk. In many cases, it would be more efficient to store only those read-pairs or reads who intersect some region around the variant locations. Alternatively, if your scientific question is focused on only one aspect of the data (e.g. breakpoints), many reads can be removed without losing the information relevant to the problem.

Address of the bookmark: https://github.com/broadinstitute/VariantBam

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

Radha Agarkar — Fri, 04 Jan 2019 10:10:48 -0600

EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.

EXCAVATOR is a novel software package for the detection of copy number variants (CNVs) from whole-exome sequencing data.
EXCAVATOR has been published on Genome Biology (http://genomebiology.com/2013/14/10/R120/abstract).

Address of the bookmark: https://sourceforge.net/projects/excavatortool/