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In this experiment, that corresponded to a tranche of ~99.0 for SNPs and ~98.0 for indels. The best-practice example documentation uses command line parameter that specify a consistent tranche of 99.0 for both SNPs and indels, so depending on which you follow as a default you’ll get different sensitivities. For SNPs, removing low complexity regions removes approximately ~2% of the total calls for both FreeBayes and GATK. Bcbio-nextgen handles installation and automation of the programs used in this comparison. The documentation contains instructions to download the data and run the NA12878 trio calling and validation.
IPython parallel provides the distributed framework for creating these processing setups, working on top of existing schedulers like LSF, SGE and TORQUE. It creates processing engines on distributed cores within the cluster, using ZeroMQ to communicate job information between machines. We worked toward inclusion of the EDAM onotology as part of the Mobyle system’s built-in type and classification mechanisms.
Variant callers
Evaluation of the reference materials with FreeBayes and other callers can help reduce potential GATK-specific biases when continuing to develop reliable reference materials. The most recent versions of FreeBayes have improved sensitivity and specificity which puts them on par with GATK HaplotypeCaller. One area where FreeBayes performs better is in correctly resolving heterozygote/homozygote calls, reflected in the lower number of discordant shared variants. Alternatively, they may represent persistent errors found in multiple callers. Joint calling – Calling a group of samples together with algorithms that do not need simultaneous access to all population BAM files.
This replicates Heng’s results and Michael’s assessment of common errors in whole genome samples, and indicates we need to specifically identify and assess the 2% of the genome labeled as low complexity. Practically, we’ll exclude them from further evaluations to avoid non-representative bias, and suggest removing or flagging them when producing whole genome variant calls. Michael Linderman and colleagues describe approaches for validating clinical exome and whole genome sequencing results. One key result I took from the paper was the difference in assessment between exome and whole genome callsets. Coverage differences due to capture characterize discordant exome variants, while complex genome regions drive whole genome discordants. Reading this paper pushed us to evaluate whole genome population based variant calling, which is now feasible due to improvements in bcbio-nextgen scalability.
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To get all these latest trending private jobs in India just register with our job portal. Iam very thankful to freshersworlds.It Is a very good and geniune platform for freshers to find jobs... $ mkdir work && cd work $ bcbio_nextgen.py bcbio_system.yaml ../input ../config/NA12878-exome-methodcmp.yaml -n 8 The bcbio-nextgen documentation describes how to parallelize processing over multiple machines using cluster schedulers . Providing detailed timing estimates for large, heterogeneous pipelines is difficult since they will be highly depending on the architecture and input files.
This input configuration file should be easily adjusted to run on your data of interest. We plan to continue working with the open source scientific community to integrate, extend and improve these tools and are happy for any feedback and suggestions. Samtools (1.0) – The recently released version of samtools and bcftools with a new multiallelic calling method. John Marshall, Petr Danecek, James Bonfield and Martin Pollard at Sanger have continued samtools development from Heng Li’s code base. John Kern and other members of the bcbio community tested, debugged and helped identify issues with the implementation. Evaluating calling and tumor-only prioritization on Horizon reference standards.
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Minimal post-processing, with de-duplication using samtools rmdup and no realignment or recalibration. The Ensemble calling method provides the best variant detection by combining inputs from GATK UnifiedGenotyper, HaplotypeCaller and FreeBayes. Tying all these parts together, the bcbio-nextgen-vm wrapper drives processing of individual run components using isolated Docker containers. The Python wrapper script uses the existing work in bcbio-nextgen for defining workflows, and it runs on distributed cluster systems using the IPython parallel framework. Using Conda and Binstar to handle installation of Python dependencies results in a streamlined installation procedure for all the wrapper software. The pre-built Docker image contains a full manifest of installed software, from the system libraries to custom scientific packages.
The IPython parallel framework manages the set up of parallel engines and handling communication between them. These abstractions allow the same pipeline to scale from a single processor to hundreds of node on a cluster. We also worked to build a tool that helps provide run time estimations for bioinformatcs jobs (e.g. “how long should aligning 40 million reads against hg19 with BWA take if I use 8 cores?”). We plan to collaborate on longer term development of this with the Genome Comparison of Analytic Testing team. This results from allele differences, such as heterozygote versus homozygote calls, or variant identification differences, such as indel start and end coordinates. With this framework in place, the next step for improving reproducibility is enabling full provenance to trace processing steps.
Bcbio-nextgen-vm drives the workflow and parallel runs, interacting with a cluster scheduler, and lives outside of Docker on a central server. The wrapper code manages the work of starting Docker containers and mounting external filesystems to local mounts within the Docker container. On each processing node, execution happens within isolated Docker containers with external biological software and bcbio-nextgen processing-specific code. Using hard filtering of variants based on GATK recommendations performs well and is also a good default choice. For SNPs, the hard filter defaults are less conservative and more in line with FreeBayes results than VQSR defaults.
We achieve this by fostering a healthy and safe working space for them to succeed and grow. Above all, working at GSK means working with colleagues, friends and managers who support, motivate and care for one another every step of the way. Everyone at GSK is encouraged to drive their development through a blend of on-the-job experiences, plus best-in-class formal and informal learning. Steampunk offers cloud, cybersecurity, data exploitation, DevSecOps, platform, and human-centered design services for Federal government agencies. PNC is a financial services corporation that works with retail, business, and corporate clients and has assets totaling more than $290 billion.
We plan to provide specific suggestions and feedback to samtools, Platypus and other tool authors as part of a continuous validation and feedback process. At this scale Lustre and NFS have similar throughput, with Lustre outperforming NFS during IO intensive steps like alignment, post-processing and large BAM file merging. From previous benchmarking work we’ll need to process additional samples in parallel to fully stress the shared filesystem and differentiate Lustre versus NFS performance. However, the resource plots at this scale show potential future bottlenecks during alignment, post-processing and other IO intensive steps. Generally, having Lustre scaled across 4 LUNs per object storage server enables better distribution of disk and network resources.
Bcbio-nextgen currently has extensive log files of command lines and program output, but in parallel environments it requires work to deconvolute these to establish the full set of steps leading up to production of files of interest. With the new isolated framework, you can install bcbio-nextgen on a system with only Docker installed. Conda handles installation of the Python dependencies, ideally inside of an isolated minimal Anaconda Python environment, and is the only non-Docker-contained infrastructure required. The install script will also download and prepare biological data required for processing, including genomes, index files and annotations. The diagram below shows the parts of bcbio-nextgen handled within each of the components of the system.
This was a useful change in mindset for me as I’ve primarily thought about removing low quality, low depth variants. However, high depth regions can indicate potential copy number variations or hidden duplicates which result in spurious calls. Overall, this comparison identifies areas where we can hope to improve generalized joint calling.
We encourage reanalysis and welcome suggestions for improving the presentation and conclusions in this post. Summary CSV of comparisons split by methods and concordance/discordance types, easily importable into R or pandas for further analysis. This is preliminary work as we continue to optimize code parallelism and work on cluster and distributed file system setup. We welcome feedback and thoughts to improve pipeline throughput and scaling recommendations. In partnership with Dell Solutions Center, we’ve been performing benchmarking of the pipeline on dedicated cluster hardware. The Dell system has core machines connected with high speed InfiniBand to distributed NFS and Lustre file systems.
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