BOL: Related items

NGMLR: long-read mapper designed to align PacBio or Oxford Nanopore

Jit — Wed, 25 Apr 2018 07:30:54 -0500

CoNvex Gap-cost alignMents for Long Reads (ngmlr) is a long-read mapper designed to sensitively align PacBilo or Oxford Nanopore to (large) reference genomes. It was designed to quickly and correctly align the reads, including those spanning (complex) structural variations. Ngmlr uses an SV aware k-mer search to find approximate mapping locations for a read and then a banded Smith-Waterman alignment algorithm to compute the final alignment. Ngmlr uses a convex gap cost model that penalizes gap extensions for longer gaps less than for shorter ones to compute precise alignments. The gap model allows ngmlr to account for both the sequencing error and real genomic variations at the same time and makes it especially effective at more precisely identifying the position of breakpoints stemming from structural variations. The k-mer search helps to detect and split reads that cannot be aligned linearly, enabling ngmlr to reliably align reads to a wide range of different structural variations including nested SVs (e.g. inversions flanked by deletions).

Address of the bookmark: https://github.com/philres/ngmlr

Figeno: Tool for plotting sequencing data along genomic coordinates.

LEGE — Tue, 17 Sep 2024 02:28:15 -0500

Tool for plotting sequencing data along genomic coordinates.

FIGENO is a
  FIGure
    GENerator
for GENOmics

With figeno, you can plot various types of sequencing data along genomic coordinates. Video overview: https://www.youtube.com/watch?v=h1cBeXoSYTA.

Address of the bookmark: https://github.com/CompEpigen/figeno

Fundamentals of Data Visualization by Claus O. Wilke

Abhi — Sat, 08 Jun 2024 16:07:19 -0500

The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.

The entire book is written in R Markdown, using RStudio as my text editor and the bookdown package to turn a collection of markdown documents into a coherent whole. The book’s source code is hosted on GitHub, at https://github.com/clauswilke/dataviz.

Address of the bookmark: https://clauswilke.com/dataviz/

Flye: Fast and accurate de novo assembler for single molecule sequencing reads

Jit — Fri, 04 May 2018 19:16:22 -0500

Flye is a de novo assembler for long and noisy reads, such as those produced by PacBio and Oxford Nanopore Technologies. The algorithm uses an A-Bruijn graph to find the overlaps between reads and does not require them to be error-corrected. After the initial assembly, Flye performs an extra repeat classification and analysis step to improve the structural accuracy of the resulting sequence. The package also includes a polisher module, which produces the final assembly of high nucleotide-level quality.

Address of the bookmark: https://github.com/fenderglass/Flye

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/

McClintock: Meta-pipeline to identify transposable element insertions using next generation sequencing data

BioStar — Tue, 27 Oct 2020 00:21:18 -0500

an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae.

Address of the bookmark: https://github.com/bergmanlab/mcclintock

PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data

Abhi — Wed, 06 Sep 2023 00:14:28 -0500

Phylogenomic Analysis Pipeline for Herbarium Specimens

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

License: GNU General Public License

Citation:

Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1–9. https://doi.org/10.1002/aps3.11475

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

Libraries or management tools for high throughput sequencing data

LEGE — Fri, 04 Oct 2024 02:45:06 -0500

GATB Library. The Genome Analysis Toolbox with de-Bruijn graph. A large part of tools developed by the GenScale team are based on this library.
These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (e.g. metagenomes). Among them are (the full is available here: https://gatb.inria.fr/software/):
LRez: C++ Library and toolkit for the barcode-based management and indexation of linked-read datasets.

Variant calling and/or genotyping

DiscoSNP++ and discoSnpRAD: Reference-free small variant discovery (SNPs and indels)
MindTheGap: Detection and assembly of large insertion variants
TakeABreak: reference-free inversion discovery tool
SVJedi: Structural Variant genotyper with long read data
SVJedi-graph: Structural Variant genotyper with long read data using a variation graph

Sequence assembly

MinYS: reference-guided genome assembly in metagenomics data
MTG-link: local assembly tool for linked-read data
Minia: De novo short read assembler
de-novo pipeline: de-novo assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes
Mapsembler2: Targeted assembly (not maintained)

Managing k-mers & indexation

findere: simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
- fimpera extends findere adding the abundance information.
kmtricks: modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.
kmindex is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.
back to sequences: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.
Backpack Quotient Filter: k-mer indexing data structure with abundance
short read connector: Detect similar reads from potentially large read set
DSK: Count K-mer in sequences

Pangenome graph manipulation

Pancat: Pangenome Comparison and Analysis Toolkit
GFAGraphs: a Python library to handle pangenome graph files in GFA format.

Comparative metagenomics with k-mers

Simka and SimkaMin: Comparative metagenomics for large-scale datasets
Comparead & Commet: comparison of metagenomic datasets

Species and bacterial strains identification

ORI: software using long nanopore reads to identify bacteria present in a sample at the strain level
StrainFLAIR: STRAIN-level proFiLing using vArIation gRaph

General-purpose sequencing data manipulation

GASSST: long read mapper
Leon: short read compressor (now included in GATB-core)
Bloocoo: short read corrector
BCALM: Construct compacted de Bruijn graphs (unitigs)

Protein Structure

A_Purva: Contact Map Overlap solver
MD-Jeep: Distance Geometry solver
CSA: Comparative Structural Alignment

Workflow

SLICEE: parallel execution of bioinformatics workflows

Comparative Genomics

CASSIS: detection of rearrangement breakpoints
PLAST: intensive bank-to-bank sequence comparison
DRJBreakpointFinder: detection and precise localization of excision sites in proviral segments

ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data

Jit — Mon, 19 Feb 2018 06:46:15 -0600

ETE v3, featuring numerous improvements in the underlying library of methods, and providing a novel set of standalone tools to perform common tasks in comparative genomics and phylogenetics.

The new features include

(i) building gene-based and supermatrix-based phylogenies using a single command,

(ii) testing and visualizing evolutionary models,

(iii) calculating distances between trees of different size or including duplications, and

(iv) providing seamless integration with the NCBI taxonomy database.

ETE is freely available at http://etetoolkit.org

Address of the bookmark: http://etetoolkit.org