During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

There are Neanderthal bones. Denisovan remains. Fossils from ancient relatives of our species. Each specimen gives us another piece of the story of how Homo sapiens came to be.

But now imagine that one of the most important characters in that story has left behind no fossil, no skull, no bone, and no genome.

Nothing that a paleontologist could put behind glass.

And yet, somehow, that ancient population is still speaking.

It is speaking through us.

A fascinating new study published in Science (https://www.science.org/doi/10.1126/science.aef8874?) introduces a computational method called TRACE, which allows scientists to search modern human genomes for genetic traces of ancient populations that have never been directly identified.

For years, scientists have known that our ancestors interacted with Neanderthals and Denisovans. Their DNA still survives in modern human populations. But these may not have been the only ancient humans who crossed paths with our ancestors.

Using TRACE, researchers found evidence of an unknown archaic population that contributed DNA to the ancestors of modern humans before humans spread beyond Africa.

Even more surprisingly, they found evidence of a much older lineage—possibly around 1.8 million years old—whose genetic legacy may have reached some modern humans indirectly through Denisovans.

The extraordinary part is that researchers did not need DNA from these mysterious populations to detect them. Instead, they studied the ancestry of modern genomes and looked for genetic patterns that revealed unusually ancient branches of our family history.

It is almost like finding the footprints of someone who disappeared thousands of years ago—without ever finding the person themselves.

These discoveries suggest that human evolution was far more complicated than a simple family tree. Ancient human populations repeatedly separated, migrated, met and exchanged genes. Some eventually disappeared, but pieces of their genetic legacy survived.

And that means our DNA is more than a biological instruction manual.

It is also an archive of human history.

Some of the people recorded in that archive may never have a name or a fossil. But thanks to methods like TRACE, their genetic echoes are finally becoming visible.

The next big discovery about our ancient past may not come from a fossil in the ground.

It may already be inside us.

Reference: Zhang et al., “Recovering signatures of archaic hominin introgression using ancestral recombination graphs,” Science (2026), DOI: 10.1126/science.aef8874. Detail paper @ https://www.science.org/doi/10.1126/science.aef8874?

The TRACE repository (https://github.com/YulinZhang9806/trace) contains the Python package and command-line tools used to infer archaic admixture tracts from ancestral recombination graphs.

You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit. 

Understanding the basics of genome sequencing:

Tutorial by Luke Jostins.

http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/

http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/

A window into third-generation sequencing

http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf

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NGS data analysis pipelines

• Detecting and annotating genetic variations using the HugeSeq pipeline  DOI: 10.1038/nbt.2134
• NARWHAL, a primary analysis pipeline for NGS data http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc
• RseqFlow: Workflows for RNA-Seq data analysis  DOI: 10.1093/bioinformatics/btr441
• ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence  10.1186/1471-2164-12-285
• A framework for variation discovery and genotyping using next-generation DNA sequencing data  PubMed: 21478889
• SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects  DOI: 10.1186/1471-2105-12-134 Abstract: http://www.biomedcentral.com/1471-2105/12/134/abstract
• WEP: a high-performance analysis pipeline for whole-exome data http://www.biomedcentral.com/1471-2105/14/S7/S11
• DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. http://www.ncbi.nlm.nih.gov/pubmed/23657089
• GATK: a Toolkit for Genome Analysis http://www.broadinstitute.org/gatk/
• Metagenomics:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/
• RNASeq:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/
• Bioinformatics and Seq courses: http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html
• Variant Detection (Model organism) Advanced tutorial https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM
• Variant Detection Introductory tutorial https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI
• Microbial de novo Assembly for Illumina Data Introductory tutorial https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM
• RNAseq Differential Gene Expression Introductory tutorial https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y

" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ". 

Address of the bookmark: http://chagall.med.cornell.edu/NGScourse/

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

• data = mock data for the snakefile to use
• Snakefile = name of the snakemake “formula” file
  • Note: The default file that snakemake looks for in the current working directory is the Snakefile. If you would like to override that you can specify it following the -s
    • snakemake -s snakefile.py
• envs = directory for storing the conda environments that the workflow will use.
• scripts = directory for storing python scripts called by the snakemake formula.
• config.json = json format file with extra parameters for our snakemake file to use.
• cluster.json = json format file with specification for running on the HPC
• samples.txt = file we will use later relating to the config.json file.

Run the snakemake file as a dry run (the example workflow shown above).

• This will build a DAG of the jobs to be run without actually executing them.
• snakemake --dry-run

User can execute rules of interest.

• snakemake --dry-run all VS. snakemake --dry-run call VS. snakemake --dry-run bwa

Run the snakemake file in order to produce an image of the DAG of jobs to be run.

• snakemake --dag | dot -Tsvg > dag.svg OR snakemake --dag | dot -Tsvg > dag.svg

Run the snakemake (this time not as a dry run)

1. snakemake --use-conda

Address of the bookmark: https://github.com/alesssia/YAMP

Abstract: Strand NGS includes comprehensive workflows for DNA-Seq, RNA-Seq, Small RNA-Seq, ChIP-Seq, MeDIP-Seq, and Methyl-Seq analysis. Each workflow includes a quality assessment and filter section, followed by a workflow-specific analysis section. The pipeline functionality in Strand NGS allows users to execute a sequence of analysis steps with specific parameters - all without any manual intervention. This simplifies the analysis in large scale sequencing projects where every sample needs to be processed identically.

In this webinar we will discuss the pre-packaged pipelines present in Strand NGS. The packaged pipelines have well-chosen default parameters and are suitable for users analyzing data for the first time in the tool. We will also show how advanced users can customize pipelines and share them with other Strand NGS users. Finally, we will show a brief glimpse of an elaborate pipeline that aligns reads, filters poor-quality matches, computes coverage metrics, identifies variants, checks for sample cross-contamination, and emails quality reports - all from within Strand NGS.

Speaker: Dr. Vamsi Veeramachaneni, Vice President - Bioinformatics, Strand Life Sciences

Details: Session 1: 2:30 PM IST, Session 2 : 10:30 PM IST
Register here: http://www.strand-ngs.com/webinar_registration

Address of the bookmark: https://pgapx.ybzhao.com/