https://github.com/pditommaso/awesome-pipeline

Address of the bookmark: https://github.com/pditommaso/awesome-pipeline

Address of the bookmark: https://github.com/cschin/Peregrine

BlobToolKit is comprised of four components:

1. BlobToolKit Viewer allows browser-based interactive visualisation and filtering of preliminary or published genomic datasets even for highly fragmented assemblies.
2. BlobTools2 is a command-line program to convert assemblies and analysis results into datasets that can be further processed using BlobTools2 and/or visualised in the Viewer.
3. The BlobToolKit Specification features a formal schema and validator for the JSON-based BlobDir format used by BlobTools2 and the Viewer.
4. The BlobToolKit Pipeline is a configurable Snakemake pipeline that automates all steps from retrieving public datasets through running analyses and generating a BlobDir dataset with BlobTools2, ready for visualisation in the Viewer.

Paper https://www.biorxiv.org/content/10.1101/844852v1.full.pdf

Address of the bookmark: https://blobtoolkit.genomehubs.org/

It is enormous, billions of letters long. Some of those letters are easy to understand because they contain instructions for building proteins, the tiny machines that keep our cells alive.

But what if some of the most important instructions aren't in those obvious chapters?

Scientists have long suspected that the mysterious stretches of DNA between protein-coding genes may contain some of life's most interesting secrets. These regions can act like switches, telling genes when, where, and how strongly to turn on. Among them are conserved non-coding elements, or CNEs DNA sequences that have remained remarkably similar across different species.

Think of a CNE as a sentence that nature has copied into several editions of the same book.

A mouse has it.

A bird has it.

A human has it.

And despite millions of years of evolution, much of the sentence remains almost unchanged.

That conservation is a clue: this piece of DNA probably matters.

But there was a problem. Finding these hidden sentences across enormous genomes is difficult. Researchers needed to compare many genomes, identify conserved regions, and then determine which of those regions might have evolved unusually quickly in particular branches of the evolutionary tree.

Enter CNEwrap. https://github.com/YanCCscu/CNEwrap

The researchers developed CNEwrap as a streamlined toolkit capable of handling large-scale genome comparisons, conserved-element detection, and evolutionary analysis. Inside it is a new algorithm called EvoAcc, designed to identify CNEs that show accelerated evolution in specific species or lineages.

Why does that matter?

Because evolution isn't just about what stays the same.

Sometimes, the most interesting story is hidden in what changed.

Imagine that nearly every species carries the same regulatory sentence, but one lineage suddenly has several unusual edits. Perhaps those changes helped shape a new body structure, a different adaptation, or another distinctive trait.

EvoAcc is designed to help researchers find these evolutionary clues. In the study's tests, it performed particularly well in scenarios involving two or three accelerated lineages and showed improved sensitivity to insertion and deletion mutations compared with several existing approaches.

The researchers also tested CNEwrap using functional genomic regions from mammals and found that EvoAcc could recover human-specific accelerated regions while identifying some signals that other methods missed.

So the story of CNEwrap is really a story about looking beyond the obvious.

For decades, scientists have often focused on the DNA that directly encodes proteins. But the genome is more like a vast library: the protein-coding genes are the main text, while non-coding regions may contain punctuation, instructions, switches, annotations, and editing notes that tell the cell how to read the book.

CNEwrap gives researchers a new way to search that hidden material.

And perhaps somewhere inside those billions of DNA letters is an evolutionary story we haven't read yet.

The exciting part is that we now have better tools to find it.

Read more about it https://academic.oup.com/nar/article/54/14/gkag709/8738333?

 http://metapred2cs.ibers.aber.ac.uk/

Address of the bookmark: https://github.com/martinjvickers/MetaPred2CS

For the full list of features, improvements and fixes, see the release notes:https://ncbi.nlm.nih.gov/tools/gbench/releasenotes

New Features

• BLAST Tool: added support for local custom BLAST databases
• Graphical Sequence View: added log scaling option for graph tracks
• Generic Table View: new tutorial added

Bug Fixes and Improvements

• Project Tree View: Genomic Collections/Assemblies now show accessions, not just names
• Tree View: layout updated to better accommodate nodes of different sizes
• Table Import Dialog (MacOS): fixed issue with table visibility
• Fixed bug where different molecules IDs in GenBank could resolve to the same sequence
• Graphical Sequence View: fixed issue where sequence track was not shown for some sequences
• Graphical Sequence View: fixed protein coloration methods
• Graphical Sequence View: improved rendering of Markers to better indicate boundaries and produce higher quality PDF images
• Create Gene Model tool: fixed scenario when gene model tool failed with local sequences
• Search View: ORF Finder – fixed incorrect protein lengths
• Fixed bug with not opening project file (.gbp) on a click
• Fixed issues in GVF import
• Fixed BLAST Search tool against NCBI databases not working
• Fixed tblastn (protein BLAST) not working in standalone mode
• Fixed GTF export failure

More at http://www.vicbioinformatics.com/software.vague.shtml

Address of the bookmark: http://www.vicbioinformatics.com/software.vague.shtml

Address of the bookmark: https://www.khanacademy.org/math/statistics-probability

• velvet (velveth velvetg should be in your PATH)
• R (with Sweave)
• pdflatex (usually part of TeTeX)
• ggplot2 (from R prompt type install.packages("ggplot2","proto","xtable"))
• Perl

Optional:

• BLAT or BLAST (to generate alignments against a reference genome). If using BLAT, add faToTwoBit,gfClient,gfServer to your PATH. If using BLAST, add blastall and formatdb.

Edit permute.sh to your liking, paying particular attention to the kmer, cvCut, expCov, and other flags

To Run:

1. perl fastaAllSize mysequences.fa > mysequences.stat or gunzip -c mysequences.fa.gz | fastaAllSize > mysequences.stat Substitute fastqAllSize for fastq files.
2. ./permute.sh mysequences (leave out the .fa)

https://github.com/leipzig/standardized-velvet-assembly-report

Address of the bookmark: https://github.com/leipzig/standardized-velvet-assembly-report

Address of the bookmark: https://github.com/vice87/gam-ngs