HLA-MA: Simple yet powerful matching of samples using HLA typing results

We propose the simple method HLA-MA for consistency checking in pipelines operating on human HTS data. The method is based on the HLA typing result of the state-of-the-art method OptiType. Provided that there is sufficient coverage of the HLA loci, comparing HLA types allows for simple, fast, and robust matching of samples from whole genome, exome, and RNA-seq data. This approach is reliable for sample re-identification even for samples with high mutational loads, e.g., caused by microsatellite instability or POLE1 defects

Contening cultures amongst development actors

Lewis et al (2003) establish a cogent argument which suggests that serious analysis of the culture of aid organizations, and of the relationships with other actors, matters, and that it is a neglected area of analysis. Their discussion raises important new questions about the development enterprise from an internal perspective that heretofore has been neglected or ignored. Contrasting the article by Lewis et al. with a book by Harrison and Huntington (2000) reinforces that conviction. Throughout the Harrison and Huntington book-- whose authors provide an excellent overview of the history of the study of culture as something that certainly does ‘matter’ in development--we kept saying to ourselves that ‘All this is fine, but it is focussed (as is much of the ancillary literature on ‘culture’ in development) on looking outward, at others undergoing development, without consideration of the development agency actors themselves. It mostly addresses questions and issues concerning the question: Why some political and national systems succeed and others fail

Sea anemone model has a single Toll-like receptor that can function in pathogen detection, NF-κB signal transduction, and development

In organisms from insects to vertebrates, Toll-like receptors (TLRs) are primary pathogen detectors that activate downstream pathways, specifically those that direct expression of innate immune effector genes. TLRs also have roles in development in many species. The sea anemone Nematostella vectensis is a useful cnidarian model to study the origins of TLR signaling because its genome encodes a single TLR and homologs of many downstream signaling components, including the NF-κB pathway. We have characterized the single N. vectensis TLR (Nv-TLR) and demonstrated that it can activate canonical NF-κB signaling in human cells. Furthermore, we show that the intracellular Toll/IL-1 receptor (TIR) domain of Nv-TLR can interact with the human TLR adapter proteins MAL and MYD88. We demonstrate that the coral pathogen Vibrio coralliilyticus causes a rapidly lethal disease in N. vectensis and that heat-inactivated V. coralliilyticus and bacterial flagellin can activate a reconstituted Nv-TLR–to–NF-κB pathway in human cells. By immunostaining of anemones, we show that Nv-TLR is expressed in a subset of cnidocytes and that many of these Nv-TLR–expressing cells also express Nv-NF-κB. Additionally, the nematosome, which is a Nematostella-specific multicellular structure, expresses Nv-TLR and many innate immune pathway homologs and can engulf V. coralliilyticus. Morpholino knockdown indicates that Nv-TLR also has an essential role during early embryonic development. Our characterization of this primitive TLR and identification of a bacterial pathogen for N. vectensis reveal ancient TLR functions and provide a model for studying the molecular basis of cnidarian disease and immunity.IOS-1354935 - National Science Foundation (NSF); GRFP - National Science Foundation (NSF); GRFP - National Science Foundation (NSF); 1262934 - National Science Foundation (NSF); 2014-BSP - Arnold and Mabel Beckman Foundatio

Digestiflow: from BCL to FASTQ with ease

Management of raw-sequencing data and its pre-processing (conversion into sequences and demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions ranging from manual management of spreadsheets to very complex and customized laboratory information management systems handling much more than just sequencing raw data. In this article, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. It allows for automated extraction of information from flow cell data and management of sample sheets. Furthermore, it allows for the automated and reproducible conversion of Illumina base calls to sequences and the demultiplexing thereof using bcl2fastq and Picard Tools, followed by quality control report generation. Availability and implementation: The software is available under the MIT license at https://github.com/bihealth/digestiflow-server. The client software components are available via Bioconda

SCelVis: Powerful explorative single cell data analysis on the desktop and in the cloud

Background: Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication. Results: To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. Methods: SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis

SCelVis: exploratory single cell data analysis on the desktop and in the cloud

BACKGROUND: Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication. RESULTS: To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data. METHODS: SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis

The ACOS CO_2 retrieval algorithm – Part II: Global X_(CO_2) data characterization

Here, we report preliminary estimates of the column averaged carbon dioxide (CO_2) dry air mole fraction, X_(CO_2), retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global X_(CO_2) field, including its variation with latitude and season. However, low yields of retrieved X_(CO_2) over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT X_(CO_2) retrievals with X_(CO_2) estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) X_(CO_2) bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global X_(CO_2) bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O_2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT X_(CO_2) retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O_2 and CO_2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in X_(CO_2) on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit

SigsPack, a package for cancer mutational signatures

BACKGROUND: Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology. RESULTS: We present here SigsPack, a Bioconductor package to estimate a sample's exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example. CONCLUSION: SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization

Identification and ranking of recurrent neo-epitopes in cancer

BACKGROUND: Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system’s capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. METHOS: We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions. RESULTS: We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year. CONCLUSION: While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options

Identification and ranking of recurrent neo-epitopes in cancer

Neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. We propose a method for in silico selection of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. This is achieved by carefully screening 33 TCGA data sets for recurrent somatic amino acid exchanges and, for the 1,055 resulting recurrent variants, applying MHC class I binding prediction algorithms. A preliminary confirmation of epitope binding and recognition by CD8 T cells has been carried out for a couple of candidates in humanized mice. Recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options