Pubmed highlight: SNP detection tools comparison

Performance comparison of SNP detection tools with illumina exome sequencing data-an assessment using both family pedigree information and sample-matched SNP array data.

Nucleic acids research. 2014 May 15. pii: gku392

Abstract

To apply exome-seq-derived variants in the clinical setting, there is an urgent need to identify the best variant caller(s) from a large collection of available options. We have used an Illumina exome-seq dataset as a benchmark, with two validation scenarios-family pedigree information and SNP array data for the same samples, permitting global high-throughput cross-validation, to evaluate the quality of SNP calls derived from several popular variant discovery tools from both the open-source and commercial communities using a set of designated quality metrics. To the best of our knowledge, this is the first large-scale performance comparison of exome-seq variant discovery tools using high-throughput validation with both Mendelian inheritance checking and SNP array data, which allows us to gain insights into the accuracy of SNP calling through such high-throughput validation in an unprecedented way, whereas the previously reported comparison studies have only assessed concordance of these tools without directly assessing the quality of the derived SNPs. More importantly, the main purpose of our study was to establish a reusable procedure that applies high-throughput validation to compare the quality of SNP discovery tools with a focus on exome-seq, which can be used to compare any forthcoming tool(s) of interest.

Pubmed highlight: Comparison of mapping alghoritms

Map millions of reads to some reference genome sequence is in most cases the first step in NGS data analysis. Proper mapping is essential for downstream variant identification and assessing of the quality of each sequenced base. Various tools exist to perform this task and this paper present an interesting new tool to benchmark results from the different aligners. Authors developed CuReSim, a tool able to generate simulated reads dataset resembling differetn NGS technologies, and CuReSimEval, that perform the performance evaluation for a given aligner given the created dataset.
In the paper they apply this new method to compare the performance of some popular aligners (like BWA, TMAP and BowTie) working on Ion Torrent data. "The application of this procedure to Ion Torrent data from the whole genome sequencing of small genomes [...] demonstrate that it is helpful for selecting a mapper based on the intended application, questions to be addressed, and the technology used." they reports in the abstract.

Comparison of mapping algorithms used in high-throughput sequencing: application to Ion Torrent data.

Caboche S, Audebert C, Lemoine Y, Hot D

Abstract

BACKGROUND: The rapid evolution in high-throughput sequencing (HTS) technologies has opened up new perspectives in several research fields and led to the production of large volumes of sequence data. A fundamental step in HTS data analysis is the mapping of reads onto reference sequences. Choosing a suitable mapper for a given technology and a given application is a subtle task because of the difficulty of evaluating mapping algorithms.

RESULTS: In this paper, we present a benchmark procedure to compare mapping algorithms used in HTS using both real and simulated datasets and considering four evaluation criteria: computational resource and time requirements, robustness of mapping, ability to report positions for reads in repetitive regions, and ability to retrieve true genetic variation positions. To measure robustness, we introduced a new definition for a correctly mapped read taking into account not only the expected start position of the read but also the end position and the number of indels and substitutions. We developed CuReSim, a new read simulator, that is able to generate customized benchmark data for any kind of HTS technology by adjusting parameters to the error types. CuReSim and CuReSimEval, a tool to evaluate the mapping quality of the CuReSim simulated reads, are freely available. We applied our benchmark procedure to evaluate 14 mappers in the context of whole genome sequencing of small genomes with Ion Torrent data for which such a comparison has not yet been established.

CONCLUSIONS: A benchmark procedure to compare HTS data mappers is introduced with a new definition for the mapping correctness as well as tools to generate simulated reads and evaluate mapping quality. The application of this procedure to Ion Torrent data from the whole genome sequencing of small genomes has allowed us to validate our benchmark procedure and demonstrate that it is helpful for selecting a mapper based on the intended application, questions to be addressed, and the technology used. This benchmark procedure can be used to evaluate existing or in-development mappers as well as to optimize parameters of a chosen mapper for any application and any sequencing platform.

PubMed Highlight: Importance of annotation tool and transcript dataset for variants analysis

This paper recently published on Genome Medicine analyzes the differences in variants annotation when using different annotation tools and transcript datasets. Authors extensively report on pitfalls and peculiar aspects of the two mostly diffused softwares (ANNOVAR and VEP), using transcripts definition from both RefSeq and Ensembl databases.
From the paper abstract clearly emerges a high level of discrepancy, expecially for functional variants (namely missense and LoF) that are the far the most relevant in NGS analysis. "We find only 44% agreement in annotations for putative loss-of-function variants when using the REFSEQ and ENSEMBL transcript sets as the basis for annotation with ANNOVAR. The rate of matching annotations for loss-of-function and nonsynonymous variants combined is 79% and for all exonic variants it is 83%. When comparing results from ANNOVAR and VEP using ENSEMBL transcripts, matching annotations were seen for only 65% of loss-of-function variants and 87% of all exonic variants, with splicing variants revealed as the category with the greatest discrepancy" authors write.

What impress me the most is the low level of concordance between different transcript datasets, reflecting the fact that also the annotation of mRNA forms are far from definitely established.
So be careful with your annotations!

Here the full paper:

Choice of transcripts and software has a large effect on variant annotation
Davis J McCarthy, Peter Humburg, Alexander Kanapin, Manuel A Rivas, Kyle Gaulton, The WGS500 Consortium, Jean-Baptiste Cazier and Peter Donnelly

Some useful tools for your everyday NGS analysis

There are a lot of tools that can assist you at every step of an NGS data analysis. Here are some interesting pieces of software I've recently started using.

SAMStat - published on Bioinformatics
This tool take your sam/bam/fastq files and compute several metrics describing frequency and distribution of bases across all your reads. Results include: stats on MAPQ of reads, distribution of MAPQ across read lenght, nucleotide over-representation across reads, error distribution and identification of over-represented 2-mers and 10-mers. All the data are conveniently presented in a html summary and help you identifying potential issues present in your sequencing experiment. Moreover, these graphs are always useful as quality reports for your presentations!

ngsCAT - published on Bioinformatics
This command line tool provide you with a detailed analysis of your mapped reads given a defined target regions. It compute several metrics and stats: medium coverage, number of bases covered at least n fold, duplicated reads, distribution of on-target reads across chromosomes and uniformity of coverage across target regions. The tool require a bam file and a bed file as inputs and produce several graphs and tabs and a final summary report in html format. Really simple and useful to assess quality of your target capture!

GRAB - published on PLOS ONE
This tool is as simple as cleaver. It takes genotyping information of subjects from various formats (Genotype/Var/masterVar/GVF/VCF/PEDMAP/TPED) and compute their eventual relationship. It works best with whole genome data, but I've tested it also using vcf files from exome sequencing and reducing the default reading window it performs well at least in identifying 1st and 2nd grade relationships. This tool require R installed in your system and it's really fast in performing the analysis. It is useful when you are dealing with a group of samples and you want to verify that there are no members from the same family.

MendelScan - published on American Journal of Human Genetics
This tool is also described by the author on his blog MassGenomics. This software perform variant prioritization for family based exome sequencing studies. It needs some preliminary steps to prepare the necessary input files: a multisample vcf files with variants from the family members, a ped file describing the relations between samples, a ranked list of genes that are mostly expressed in your tissue of interest and the VEP annotated list of your variants. Given these data the tool compute a ranked list of identified variants based on the selected inheritance model (recessive or dominant). Moreover it include two additional modules developed by the authors: the Rare Heterozygous Rule Out and the Shared Identity-by-Descent. The first one "identifies candidate regions consistent with autosomal dominant inheritance based on the idea that a disease-causing haplotype will manifest regions of rare heterozygous variants shared by all affecteds, and an absence of homozygous differences between affected pairs (which would indicate that a pair had no haplotype in common).", while the second one "uses BEAGLE FastIBD results to identify regions of maximum identity-by-descent (IBD) among affected pairs". This tool integrates the canonical prediction scores (such as Polyphen, PhyloP and so on) with gene expression ranking and the newly developed methods to provide a straightforward analysis for your mendelian disease NGS studies!

SPRING - published on PLOS Genetics
Like MendelScan, this is another tool for variant prioritization. This one has also a version working from the web for those that are not familiar with the command line. The tool takes a list of seed genes already known to be involved in the pathology or in similar phenotypes and a list of your candidate missense variants and give you a ranked list of the variants that have a high probability of being associated with the disease. This tools work fine for disease with high genetic heterogeneity so that you can easily and confidently build a list of seed genes. SPRING can then be really useful in prioritizing candidates variants emerging from new studies.

PRADA - published on Bioinformatics
This tool is focused on RNA-Seq and it provide a complete framework for analysis of this kind of data. The tool is a complete solution since it can perform several kind of analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. As the project page reports:
"PRADA currently supports 7 modules to process and identify abnormalities from RNAseq data:
preprocess: Generates aligned and recalibrated BAM files.
expression: Generates gene expression (RPKM) and quality metrics.
fusion: Identifies candidate gene fusions.
guess-ft: Supervised search for fusion transcripts.
guess-if: Supervised search for intragenic fusions.
homology: Calculates homology between given two genes.
frame: Predicts functional consequence of fusion transcript"

This is a good starting point for those not familiar with RNA-Seq!

A gold standard dataset of SNP and Indels for benchmarking tools

One of the pain in the NGS data analysis is that different tools, different settings and different sequencing platforms produce different and often low overlapping variants. Every analysis pipeline has its peculiar issues and results in specific false positive/negative calls.

The need for a gold standard reference of SNP and indels calls is thus of primary relevance to correctly asses the accuracy and sensibility of new NGS pipeline. The need for a robust benchmarking of variant identification pipelines is even more critical as NGS analysis is fast moving from research to diagnostic/clinical field.

In this interesting paper on Nature Biotechnology, authors have performed the analysis of 14 different variants datasets from the same standard genome NA12878 (choose as the standard reference genome by the Genome in a Bottle Consortium) to produce a list of gold standard SNPs and Indels and identify genomic regions that are particularly difficult to address. The final dataset is the most robust produced so far since it integrate data from 5 different sequencing technology, 7 read mapper and 3 variant callers to obtain a robust estimation of SNVs.
The final standard for evaluation of your favorite pipeline is finally here, publicly available Genome Comparison and Analytic Testing website!

Nat Biotechnol. 2014 Feb 16. 

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls.

Zook JM, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, Salit M. 

Abstract

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.

Flash Report: Loblolly pine genome is largest ever sequenced

Below is the report from CBSNews.com:

Scientists say they've generated the longest genome sequence to date, unraveling the genetic code of the loblolly pine tree.
Conifers have been around since the age of the dinosaurs, and they have some of the biggest genomes of all living things.
Native to the U.S. Southeast, the loblolly pine (Pinus taeda) can grow over 100 feet (30 meters) tall and has a lengthy genome to match, with 23 billion base pairs. That's more than seven times the size of the human genome, which has 3 billion base pairs. (These pairs form sequences called genes that tell cells how to make proteins.)
"It's a huge genome. But the challenge isn't just collecting all the sequence data. The problem is assembling that sequence into order," study researcher David Neale, a professor of plant sciences at the University of California, Davis, said in a statement.
To simplify this huge genetic puzzle, Neale and colleagues assembled most of the sequence from part of a single pine nut-- a haploid part of the seed with just one set of chromosomes to piece together.
The new research showed that the loblolly genome is bloated with repetitive DNA. In fact, 82 percent of the genome repeats itself, the researchers say.
Understanding the loblolly pine's genetic code could lead to improved breeding of the tree, which is used to make paper and lumber and is being investigated as a potential biofuel, the scientists say.
The loblolly pine joins other recently sequenced conifers, including the Norway spruce (Picea abies), which has 20 billion base pairs. For their next project, the researchers are eyeing the sugar pine, a tree with 35 billion base pairs.
The research was detailed this week in the journals Genetics and Genome Biology.

Flash Report: King Richard III genome is going to be sequenced soon

The list of famous people whose genome has been sequenced is going to get a new member, more precisely a royal one. After the discovery of the remains of his body under a car park, scientists of the University of Leicester are going to sequence the genome of King Richard III, who died in the Battle of Bosworth Field in 1485. The project has the purpose to learn more about King's ancestry and health, and to provide genetic data useful for historians, researchers and the public.
In addition to the scientific aspects, these types of initiatives also represent good strategies to attract the attention of the media on the institution involved in the sequencing and, why not, to increase the possibility to raise funds for other projects with a deeper scientific impact.
In general, the choice to sequence the genome of famous historic characters could be a good initiative to acknowledge what they did for their country and a great opportunity of visibility for the institution performing the study. Here is the link to the Reuters news release.

Genapsys reveals GENIUS at AGBT

At the last AGBT meeting Genapsys, a small company from California, presented its new "launch-box" sequencer called GENIUS (Gene Electronic Nano-Integrated Ultra-Sensitive).

This machine, with the size of a toaster, is a new kind of NGS sequencer that apply electronic sensor technology and promise to produce up to 100 Gb of sequences in few hours, with read length up to 1000 bp. Like the MinION they propose to push the NGS market even further, providing a new generation of sequencher which are incredibly small, cheap and easy to operate. A step further to the so called "freedom of sequencing".

Genapsys started an early-access program for the GENIUS platform last week and plans to ship out the first instruments within a few months, followed by a general commercial launch either this year or next year.

The system has an opening in the front to insert a small, square semiconductor-based sequencing chip. A reusable reagent cartridge attaches to the back, and a computer for data processing is integrated. Based on the Genapsys CEO interview at InSequence, library preparation require clonal amplification of the template DNA on beads in an emulsion-free off-instrument process that requires no specialized equipment from the company. While the first version of the Genius will require separate sample and library preparation, the company plans to integrate sample prep into the platform eventually. The template beads are then pipetted into the chip, which has "millions of sensors" and a flat surface with no wells. There, the beads assemble into an array-like pattern, with each bead being individually addressable. This is followed by polymerase-based DNA synthesis where nucleotides are added sequentially. The system uses an electronic detection method to identify which nucleotide was incorporated, but the detection principle remain secreted by now, even if it is not based on pH measurements (like the Ion Torrent technology).

Three types of chips will be available for the Genius, generating up to 1 gigabase, 20 gigabases, or 100 gigabases of data with the sequencing run taking only few hours. Estimated sequencing costs per gigabase will be $300 for the smallest chip, $10 for the middle chip, and $1 for the largest chip. Even if the instrument price has not been determined yet but, Genapsys will provide special offers to customers committing to high usage. This will be something like an usage plan that include the instrument at a lower price, but with a minimum expense in consumable per year. Esfandyarpour, Genapsys CEO, said they have already generated sequence data on the platform, though he did not provide specifics. The data quality will be "equivalent or better than the best product out there," he said.

Uzbekistan apply genetic testing to select for future olympians

Strange, but true!

According to this news reported by The Atlantic, beginning in 2015, Uzbekistan will incorporate genetic testing into its search for Olympic athletes.

Rustam Muhamedov and his colleagues from Uzbekistan's Institute of Bioorganic Chemistry's genetics laboratory are working on developing a set of 50 genes to determine what sport a child is best suited for. So the national trainers would start working on high predisposed children and know exactly which sport is more suited to their physical characteristics.

"Developed countries throughout the world like the United States, China, and European countries are researching the human genome and have discovered genes that define a propensity for specific sports," Muhamedov tells the Atlantic. "We want to use these methods in order to help select our future champions."

The program, overseen by Uzbekistan's Academy of Sciences, would be "implemented in practice" in early 2015 in cooperation with the National Olympic Committee and several of the country's national sports federations—including soccer, swimming, and rowing.

By now there is no explicit ban against genetic testing or genetic selection on athletes, even if
the World Anti-Doping Agency discourages such practices. In the past, there was suspicions that also China government had applied some sort of genetic testing or at least encouraged marriage and pregnancy between people with desired predisposition with the aim to generate better athletes (see the Yao Ming story as example).

Many experts doubt that genetic testing could really improve performance more than an excellent training program, given that the genotype-phenotype correlation on many physiological traits relevant for sportsmen is not yet explored nor fully understood.

The explicit use of genetic testing however pose some ethical question and claim for an official rule on its application in sport practice.

We'll see if the Uzbekistan effort will push the country at the top of olympic ranking! 

Follow in real time the 2014 AGBT Meeting

The 15th annual Advances in Genome Biology and Technology (AGBT) meeting is being held in Marco Island, Florida, on February 12 -15. You can follow the latest announcements via Twitter from the top tab we placed in our blog page (the tab will be removed at the end of the meeting)

You will find here a list of some among the most interesting talks, while the complete agenda is available on the official web site of the event.