Snakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters ...without modifying the workflow. It is the first system to support the use of automatically inferred multiple named wildcards (or variables) in input and output filenames.
http://snakemake.googlecode.com.
johannes.koester@uni-due.de.
Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and ...tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid.
Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.
We present Rust-Bio, the first general purpose bioinformatics library for the innovative Rust programming language. Rust-Bio leverages the unique combination of speed, memory safety and high-level ...syntax offered by Rust to provide a fast and safe set of bioinformatics algorithms and data structures with a focus on sequence analysis.
Rust-Bio is available open source under the MIT license at https://rust-bio.github.io.
koester@jimmy.harvard.edu
Supplementary data are available at Bioinformatics online.
Identification of human leukocyte antigen (HLA) types from DNA-sequenced human samples is important in organ transplantation and cancer immunotherapy and remains a challenging task considering ...sequence homology and extreme polymorphism of HLA genes. We present Orthanq, a novel statistical model and corresponding application for transparent and uncertainty-aware quantification of haplotypes. We utilize our approach to perform HLA typing while, for the first time, reporting uncertainty of predictions and transparently observing mutations beyond reported HLA types. Using 99 gold standard samples from 1000 Genomes, Illumina Platinum Genomes and Genome In a Bottle projects, we show that Orthanq can provide overall superior accuracy and shorter runtimes than state-of-the-art HLA typers. Orthanq is the first approach that allows to directly utilize existing pangenome alignments and type all HLA loci. Moreover, it can be generalized for usages beyond HLA typing, e.g. for virus lineage quantification. Orthanq is available under https://orthanq.github.io.
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6.
SimLoRD: Simulation of Long Read Data Stöcker, Bianca K; Köster, Johannes; Rahmann, Sven
Bioinformatics,
09/2016, Volume:
32, Issue:
17
Journal Article
Peer reviewed
Open access
Third generation sequencing methods provide longer reads than second generation methods and have distinct error characteristics. While there exist many read simulators for second generation data, ...there is a very limited choice for third generation data.
We analyzed public data from Pacific Biosciences (PacBio) SMRT sequencing, developed an error model and implemented it in a new read simulator called SimLoRD. It offers options to choose the read length distribution and to model error probabilities depending on the number of passes through the sequencer. The new error model makes SimLoRD the most realistic SMRT read simulator available.
SimLoRD is available open source at http://bitbucket.org/genomeinformatics/simlord/ and installable via Bioconda (http://bioconda.github.io).
Bianca.Stoecker@uni-due.de or Sven.Rahmann@uni-due.de
Supplementary data are available at Bioinformatics online.
Accurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces ...allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable-because computationally efficient-manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo.
RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq ...technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts.
Using the workflow management system Snakemake we have developed a user friendly, fast, efficient, and comprehensive pipeline for RNA-seq analysis. VIPER (Visualization Pipeline for RNA-seq analysis) is an analysis workflow that combines some of the most popular tools to take RNA-seq analysis from raw sequencing data, through alignment and quality control, into downstream differential expression and pathway analysis. VIPER has been created in a modular fashion to allow for the rapid incorporation of new tools to expand the capabilities. This capacity has already been exploited to include very recently developed tools that explore immune infiltrate and T-cell CDR (Complementarity-Determining Regions) reconstruction abilities. The pipeline has been conveniently packaged such that minimal computational skills are required to download and install the dozens of software packages that VIPER uses.
VIPER is a comprehensive solution that performs most standard RNA-seq analyses quickly and effectively with a built-in capacity for customization and expansion.
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The rapid increase in the amount of genomic data provides researchers with an opportunity to integrate diverse datasets and annotations when addressing a wide range of biological questions. However, ...genomic datasets are deposited on different platforms and are stored in numerous formats from multiple genome builds, which complicates the task of collecting, annotating, transforming, and integrating data as needed. Here, we developed Go Get Data (GGD) as a fast, reproducible approach to installing standardized data recipes. GGD is available on Github ( https://gogetdata.github.io/ ), is extendable to other data types, and can streamline the complexities typically associated with data integration, saving researchers time and improving research reproducibility.
Neuroblastoma is the most common extracranial solid tumor of childhood, and accounts for ∼15% of all childhood cancer deaths. The histone demethylase, lysine‐specific demethylase 1 (KDM1A, previously ...known as LSD1), is strongly expressed in neuroblastomas, and overexpression correlates with poor patient prognosis. Inducing differentiation in neuroblastoma cells has previously been shown to down regulate KDM1A, and siRNA‐mediated KDM1A knockdown inhibited neuroblastoma cell viability. The microRNA, miR‐137, has been reported to be downregulated in several human cancers, and KDM1A mRNA was reported as a putative target of miR‐137 in colon cancer. We hypothesized that miR‐137 might have a tumor‐suppressive role in neuroblastoma mediated via downregulation of KDM1A. Indeed, low levels of miR‐137 expression in primary neuroblastomas correlated with poor patient prognosis. Re‐expressing miR‐137 in neuroblastoma cell lines increased apoptosis and decreased cell viability and proliferation. KDM1A mRNA was repressed by miR‐137 in neuroblastoma cells, and was validated as a direct target of miR‐137 using reporter assays in SHEP and HEK293 cells. Furthermore, siRNA‐mediated KDM1A knockdown phenocopied the miR‐137 re‐expression phenotype in neuroblastoma cells. We conclude that miR‐137 directly targets KDM1A mRNA in neuroblastoma cells, and activates cell properties consistent with tumor suppression. Therapeutic strategies to re‐express miR‐137 in neuroblastomas could be useful to reduce tumor aggressiveness.
What's new?
The protein KDM1A is strongly expressed in neuroblastomas. Overexpression of this protein correlates with poor patient prognosis, as does reduced expression of the microRNA miR‐137. In this study, the authors examined whether miR‐137 might act as a tumor suppressor in neuroblastoma, via the down‐regulation of KDM1A. Their data led them to conclude that miR‐137 does directly target KDM1A mRNA levels in neuroblastoma cells. In addition, increasing the expression of miR‐137 resulted in increased apoptosis and decreased cell proliferation of neuroblastoma cells. This may provide a promising therapeutic strategy for aggressive neuroblastomas.
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