Address of the bookmark: http://www.ub.edu/dnasp/

Testing for similarities between DNA (deoxyribonucleic acid) samples from two people allows family relationships to be established – or disproved – to an extraordinarily high degree of certainty. A common use for a DNA test is to establish if a man is the biological father of a child; this is known as a paternity test. However, there are other uses for the science of DNA testing (also called genotyping), these include forensic analysis of human DNA samples, and tracking relationships amongst domesticated animals.

The order in which the bases occur in DNA is referred to as the DNA sequence. Each person is unique and just as people differ in their fingerprints, they also have a unique and slightly different DNA sequence. Half of a person’s DNA is received from their mother, and half is received from the father. However, while fingerprints have no value for establishing family relationships, the minor variations in DNA sequence are extraordinarily useful for this purpose. All cells of our body contain DNA, skin cells from the lining of the cheek provide a simple and convenient source of material.

DNA is purified from these cells and the minor variations are read out as a type of bar-code by a machine. When the net DNA ‘barcodes’ from family members are lined up next to each other it becomes clear when a child is related to biological parents because half the stripes in the bar-code like signature will line up with those of the mother, and half will line up with those of the father. On the other hand, in the absence of a biological relationship, the DNA signatures from a child and from a potential parent are not found to have 50% in common. It may be appreciated that DNA testing is the most convenient and scientifically accurate method of determining relationships between people.

Following are the list of companies who qssist in DNA testing:

DNA testing companies

• 23andMe (admixture, adoption, deep ancestry, genealogy) (health and trait reports also available in some countries)
• 24 genetics (admixture, exome sequencing, health, paternity, pharmacogenetics, whole genome sequencing) A company catering for the Spanish market
• African Ancestry (deep ancestry)
• AfricanDNA (FTDNA affiliate) (admixture, deep ancestry, genealogy)
• AncestrybyDNA (admixture, deep ancestry)
• AncestryDNA, a subsidiary of Ancestry.com (admixture, adoption, genealogy)
• Atlas Biomed (deep ancestry, diet, health and traits, sport) A test catering for the Russian market
• BritainsDNA (formerly Ethnoancestry) (admixture, deep ancestry)
• Centrillion Biosciences (aka TribeCode) (admixture, deep ancestry)
• CymruDNAWales - see BritainsDNA
• Dante Labs (exome sequencing, health, whole genome sequencing) A test aimed at the European market
• DNA Ancestry and Family Origin (FTDNA affiliate in the Middle East) (admixture, adoption, deep ancestry, full mtDNA sequencing, genealogy)
• DNA Consultants (admixture, deep ancestry)
• DNA Tribes (admixture)
• DNA Worldwide (formerly a FTDNA partner. See also Living DNA)
• Ethnoancestry - see BritainsDNA
• Family Tree DNA (admixture, adoption, deep ancestry, full mtDNA sequencing, genealogy, Y chromosome sequencing)
• Full Genomes Corporation (whole genome sequencing, Y-chromosome sequencing)
• Gene by Gene - the parent company of Family Tree DNA which now incorporates the companies previously known as DNA Traits, DNA DTC and DNA Findings (research, health, exome sequencing, whole genome sequencing)
• Genebase (deep ancestry, genealogy)
• GenoTek (admixture, genealogy, diet and fitness, family planning, health, talents and sports) A company catering for the Russian market
• Genographic Project (admixture, deep ancestry)
• Genos Research Inc (DTC whole exome sequencing; consumer focused healthcare big data spin out from Complete Genomics; Note: no genetic genealogy focus or tools)
• Guardiome (admixture, whole genome sequencing and interpretation)
• Helix (exome sequencing) US supplier of the Genographic Project Geno 2.0 Next Generation test
• iGENEA (FTDNA affiliate) (admixture, deep ancestry, genealogy)
• IrelandsDNA - See BritainsDNA (formerly Ethnoancestry)
• MyDNA Global - a new name for BritainsDNA
• Living DNA (admixture, deep ancestry) See also DNA Worldwide
• MyHeritage DNA (admixture, genealogy)
• Oxford Ancestors (deep ancestry)
• Roots for Real (admixture, deep ancestry)
• ScotlandsDNA - (formerly Ethnoancestry) (admixture, deep ancestry)
• Sorenson Genomics (laboratory services)
• Sure Genomics (whole genome sequencing and interpretation)
• TribeCode See Centrillion Biosciences
• Veritas Genetics (whole genome sequencing and interpretation)
• Xcode (Diet and Fitness, Precision medicine, Genotyping, Sequencing, Interpretation)
• YorkshiresDNA - See BritainsDNA (formerly Ethnoancestry)
• WeGene (admixture, deep ancestry, health, sports, traits) A test tailored for the East Asian market
• YSEQ (custom Y-SNPs, Y-STRs, SNP panels, whole genome sequencing)

Ra is in development since 2014 in the form of several separate components that used to be run individually.
This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.

Unlike other state-of-the-art assemblers, Ra does not have an error correction step. Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler", https://github.com/isovic/graphmap) and constructing and reducing an overlap graph (Ra layout, https://github.com/mariokostelac/ra).

Address of the bookmark: https://github.com/mariokostelac/ra-integrate/

For the original version of the pipeline and to reproduce the Hs2-HiC and the AaegL4 genomes reported in (Dudchenko et al., Science, 2017) see the original commit.

For the detailed description of the merge section see https://github.com/theaidenlab/AGWG-merge.

Address of the bookmark: https://github.com/theaidenlab/3d-dna

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

ftp://ftp.ncbi.nih.gov/genomes/

https://hgdownload.soe.ucsc.edu/downloads.html

Address of the bookmark: ftp://ftp.ncbi.nih.gov/genomes/

Address of the bookmark: http://www.zhanyuwang.xin/wordpress/index.php/2017/07/21/baum-improving-genome-assembly-by-adaptive-unique-mapping-and-local-overlap-layout-consensus/

Address of the bookmark: https://github.com/walaj/svaba

Background/Motivation:

The dearth of structural information on alpha helical membrane protein (MPs) has hindered thus far the development of reliable knowledge –based potentials that can be used for automatic prediction of trans-membrane (TM) protein structure. While algorithm for identification of TM segments is available, modelling of the domains of alpha helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. 

Results: In a cross validated test on single sequences, our trans-membrane MM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 86 proteins with supervised topology. These results compare favorably with existing methods. 

Source Code: Matlab

Conclusion/Implementation: Here discriminant data mining approach was used to predict the location and orientation of alpha helices in membrane-spanning proteins. It is based on a first order Markov model (MM) with an architecture that corresponds closely to the biological systems. The model is enriched with three types of states for the loop on the cytoplasmic side (outer loop), loop for the non-cytoplasmic side (inner side), and trans-membrane part. The closed association between the biological and Markov states allows us to infer which part of the model architecture are important to capture the information which encodes the membrane topology, and gain a better understanding of the mechanism and constraints involved. Predictor Model was established by various  Markov decoder , and assignment of the membrane helix boundaries was apparent.