BOL: Related items

Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies' sequencing data

Jit — Tue, 22 Jan 2019 06:26:55 -0600

Run a pipeline processing fast5s to a consensus in a single command.
Recommended fixed "standard" and "fast" pipelines.
Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.
Seemless distribution of tasks over local or distributed compute.
Highly configurable.
Open source (Mozilla Public License 2.0).

Documentation can be found at https://nanoporetech.github.io/katuali/.

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

Prof. Dr. med. Andreas Ramming

Wed, 07 Aug 2019 03:25:48 -0500

In many autoimmune diseases, a misdirected immune response leads to chronic inflammation and subsequently to fibrotic and degenerative tissue remodeling. Therapeutic options are available for inflammatory joint diseases, but only about 40% of patients respond to these existing therapies on a permanent basis. In the remaining cases, these therapies miss their target from the beginning or later during the course of treatment failure. There are currently no causal therapies available for the treatment of fibrotic autoimmune diseases such as systemic sclerosis. Therefore, there is an urgent need to develop new therapeutic options for the treatment of fibrotic and synovitic autoimmune diseases. His group is therefore deal with the molecular mechanisms of these misdirected signaling pathways for the development of novel, targeted therapies

http://www.medizin3.uk-erlangen.de/forschung/arbeitsgruppen/matrixbiologie-entzuendliche-signalwege-in-arthritis-und-fibrose/

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

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

FastProNGS: fast preprocessing of next-generation sequencing reads

Rahul Nayak — Sat, 26 Dec 2020 08:35:21 -0600

FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest.

Address of the bookmark: https://github.com/Megagenomics/FastProNGS

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

RNA-Seq De novo Assembly Using Trinity

Surabhi Chaudhary — Wed, 23 Mar 2016 05:53:46 -0500

Trinity, developed at the Broad Institute and the Hebrew University of Jerusalem, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so:

Inchworm assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.
Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.
Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.

More at https://github.com/trinityrnaseq/trinityrnaseq/wiki

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Download Trinity here.

Build Trinity by typing 'make' in the base installation directory.

Assemble RNA-Seq data like so:

 Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G

Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'

Address of the bookmark: https://github.com/trinityrnaseq/trinityrnaseq/wiki

NCBI Magic-BLAST

Jit — Tue, 14 Aug 2018 18:11:11 -0500

Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets

LEGE — Fri, 17 Sep 2021 01:57:02 -0500

The pipeline was developed based on a popular workflow framework Nextflow, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. LncPipe allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.

Ref https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/

Address of the bookmark: https://github.com/likelet/LncPipe

Integrative RNA and ChIP-Seq analysis of regulatory T-cells

Strand — Tue, 04 Aug 2015 05:03:19 -0500

Integrative RNA and ChIP-Seq analysis of regulatory T-cells , a Strand NGS application note describes how integrated multi-omics functionality in Strand NGS was used to find the regulatory role of FoxP3 in T-regulatory and T-helper cells. Learn how the gene expression profiles from RNA-Seq and FoxP3 DNA-protein binding sites from ChIP-Seq are integrated. For mor information, please write to us