BOL: Related items

JBrowse 2: a modular genome browser with views of synteny and structural variation

Abhi — Tue, 25 Apr 2023 20:58:52 -0500

igvjs - a create-react-app with igv package from npm installed. the igv.js is instrumented to output "DONE" to the console when finished, and to have an increased fetchSizeLimit (which is otherwise git in CRAM longread tests)
jb2-web - stock instance of jbrowse-web v1.7.5
jb1 - stock instance of jbrowse 1 v1.16.11
jb2 embedded - a create-react-app with @jbrowse/react-linear-genome-view

Address of the bookmark: https://github.com/GMOD/jb2profile

Tools to Predict the Impact of Missense Variants !

Jit — Mon, 23 Apr 2018 12:57:33 -0500

Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of in silico tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants.

Study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. Comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Following tools are useful for mis sense muation detection ...

PolyPhen‐2 (PP2)
“Predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations”

MutationTaster‐2 (MT2)
“Evaluation of the disease‐causing potential of DNA sequence alterations”

MutationAssessor (MASS)
“Predicts the functional impact of amino acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms”

LRT
“Identify a subset of deleterious mutations that disrupt highly conserved amino acids within protein‐coding sequences, which are likely to be unconditionally deleterious”

SIFT
“Predicts whether an amino acid substitution affects protein function”

GERP++
“Identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as “rejected substitutions.” Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element”

phyloP
“Compute conservation or acceleration P values based on an alignment and a model of neutral evolution”

FatHMM unweighted (FatHMM‐U)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants”

FatHMM weighted (FatHMM‐W)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants” and its weighting scheme attributes higher tolerance scores to SNVs in proteins, related proteins, or domains that already include a high fraction of pathogenic variantsh

Combined Annotation Dependent Depletion (CADD)
“CADD is a tool for scoring the deleteriousness of single‐nucleotide variants as well as insertion/deletions variants in the human genome”

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/

SeqMule: Automated human exome/genome variants detection

BioStar — Tue, 18 Feb 2020 03:22:54 -0600

SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file.

Address of the bookmark: https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/

Illuminating next generation sequencing data with Go

Rahul Agarwal — Fri, 23 Aug 2013 07:13:33 -0500

Another good lecture for Illumina sequencing data analysis from

Dan Kortschak, Bioinformatics Group, School of Molecular and Biomedical Science ,The University of Adelaide

Address of the bookmark: http://talks.biogo.googlecode.com/git/illumination/illumination.pdf

PAired-eND Assembler for DNA sequences

Neel — Wed, 06 Apr 2016 05:25:34 -0500

PANDASEQ is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.

More at https://github.com/neufeld/pandaseq

Address of the bookmark: https://github.com/neufeld/pandaseq

PANDASEQ

Shruti Paniwala — Mon, 23 Jan 2017 04:54:32 -0600

PANDASEQ assembles paired-end Illumina reads into sequences, trying to correct for errors and uncalled bases. The assembler reads two files in FASTQ format with quality information. If amplification primers were used (e.g., to isolate a variable region of the 16S gene, or the constant regions around zinc finger binding residues), they can be removed from the sequence during assembly. The final sequence will correct any uncalled bases in the overlapping region using the complementary strand. When mismatches occur in the overlapping region, the base with the better quality score is chosen.
The algorithm is as follows:

1.Find the positions where the forward and reverse primers match best above the threshold and discard the ends of the sequence, including the primer.
2.Pick and overlap to maximise the probability of the forward and reverse reads having come from a single piece of DNA.
3.Identify the masking of the end of the read with the quality score B or # as done by CASAVA and adjust the probabilities in this region.
4.Construct an assembled sequence between the primers and calculate the quality.
5.Check for various constraints, including quality, length, uncalled bases, and user-supplied modules.

http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html

Address of the bookmark: http://neufeldserver.uwaterloo.ca/~apmasell/pandaseq_man1.html

BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data

Jit — Thu, 31 May 2018 09:35:23 -0500

BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes. The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC. https://github.com/lh3/bfc

Address of the bookmark: https://github.com/lh3/bfc

High Density Sheep SNP Genotyping Chip released!!!

Rahul Agarwal — Tue, 03 Sep 2013 13:58:04 -0500

If you are working on Sheep genomics then there is a good news for you. FarmIQ in conjunction with Illumina and the International Sheep Genomics Consortium (ISGC) are today announcing completion of the “Ovine Infinium® HD SNP BeadChip”, a high definition SNP chip for ship genome. The OvineSNP50 BeadChip features over 54,241 evenly spaced probes that target SNPs, offering more than sufficient SNP density for genome-wide association studies and other applications such as genome-wide selection, determination of genetic merit, identification of quantitative trait loci, and comparative genetic studies.

The BeadChip was developed in collaboration with leading ovine researchers from AgResearch, Baylor UCSC, CSIRO, and the USDA as part of the International Sheep Genomics Consortium. It features over 54,241 evenly spaced probes that target single nucleotide polymorphisms (SNPs). More than 18,000 of these markers were discovered through sequencing reduced representation libraries with the Illumina Genome Analyzer IIx. A set of 600 SNPs were identified by BAC end sequencing and validated with Illumina GoldenGate Genotyping Assays over 403 animals from 23 breeds. The remaining SNPs were derived from the draft ovine genome.

medaka: Sequence correction provided by ONT Research

BioStar — Mon, 18 May 2020 16:28:00 -0500

medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.

Address of the bookmark: https://github.com/nanoporetech/medaka