SUMMARY: Short tandem repeat (STR) profiling is commonly performed for authentication of biomedical models of human origin, yet no tools exist to easily compare sets of STR profiles to each other or an existing database in a high-throughput manner. Here, we present STRprofiler, a Python package, command line tool, and Shiny application providing methods for STR profile comparison and cross-contamination detection. STRprofiler can be run with custom databases or used to query against the Cellosaurus cell line database. AVAILABILITY AND IMPLEMENTATION: STRprofiler is freely available as a Python package with a rich CLI from PyPI https://pypi.org/project/strprofiler/ with source code available under the MIT license on GitHub https://github.com/j-andrews7/strprofiler and at https://zenodo.org/records/10989034. A web server hosting an example STRprofiler Shiny application backed by a database with data from the National Cancer Institute-funded PDXNet consortium and The Jackson Laboratory PDX program is available at https://sj-bakerlab.shinyapps.io/strprofiler/. Full documentation is available at https://strprofiler.readthedocs.io/en/latest/.

Deep learning models are extensively used for analyzing hematoxylin and eosin stained whole slide images. They enjoy high prediction accuracies, but may suffer large performance drops when applied to out-of-sample data. Here we systematically investigate how resolution differences between train and test sets may affect lung cancer subtype predictions from whole slide images using a transfer learning model based on the Inception V3 network. We observe models trained on blurred images perform well when applied to test sets with similar blurrings, but suffer poor predictions when applied to images with large blurring differences. In particular, we observed low area under curve values when models trained on blurred images were applied to non-blurred images.

MOTIVATION: Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed. RESULTS: We developed pyBedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph or a bigWig file. When tested on 12 ChIP-seq, ATAC-seq, RNA-seq and ChIA-PET datasets, pyBedGraph is on average 260 times faster than the existing program pyBigWig. On average, pyBedGraph can look up the exact mean signal of 1 million regions in ∼0.26 s and can compute their approximate means in <0.12 s on a conventional laptop. AVAILABILITY AND IMPLEMENTATION: pyBedGraph is publicly available at https://github.com/TheJacksonLaboratory/pyBedGraph under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics has become an indispensable part of life science over the past 2 decades. However, bioinformatics education is not well integrated at the undergraduate level, especially in liberal arts colleges and regional universities in the United States. One significant obstacle pointed out by the Network for Integrating Bioinformatics into Life Sciences Education is the lack of faculty in the bioinformatics area. Most current life science professors did not acquire bioinformatics analysis skills during their own training. Consequently, a great number of undergraduate and graduate students do not get the chance to learn bioinformatics or computational biology skills within a structured curriculum during their education. To address this gap, we developed a module-based, week-long short course to train small college and regional university professors with essential bioinformatics skills. The bioinformatics modules were built to be adapted by the professor-trainees afterward and used in their own classes. All the course materials can be accessed at https://github.com/TheJacksonLaboratory/JAXBD2K-ShortCourse.

RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0.95, while GraphClust achieves only a V-measure of 0.083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC.

SUMMARY: We present CloudNeo, a cloud-based computational workflow for identifying patient-specific tumor neoantigens from next generation sequencing data. Tumor-specific mutant peptides can be detected by the immune system through their interactions with the human leukocyte antigen complex, and neoantigen presence has recently been shown to correlate with anti T-cell immunity and efficacy of checkpoint inhibitor therapy. However computing capabilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding their role. This challenge has grown as cancer datasets become increasingly abundant, making them cumbersome to store and analyze on local servers. Our cloud-based pipeline provides scalable computation capabilities for neoantigen identification while eliminating the need to invest in local infrastructure for data transfer, storage or compute. The pipeline is a Common Workflow Language (CWL) implementation of human leukocyte antigen (HLA) typing using Polysolver or HLAminer combined with custom scripts for mutant peptide identification and NetMHCpan for neoantigen prediction. We have demonstrated the efficacy of these pipelines on Amazon cloud instances through the Seven Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfaces for running and editing, infrastructure for workflow sharing and version tracking, and access to TCGA data. AVAILABILITY AND IMPLEMENTATION: The CWL implementation is at: https://github.com/TheJacksonLaboratory/CloudNeo. For users who have obtained licenses for all internal software, integrated versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform (https://cgc.sbgenomics.com/, recommended version) can be obtained by contacting the authors. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: It has been increasingly appreciated that coding sequences harbor regulatory sequence motifs in addition to encoding for protein. These sequence motifs are expected to be overrepresented in nucleotide sequences bound by a common protein or small RNA. However, detecting overrepresented motifs has been difficult because of interference by constraints at the protein level. Sampling-based approaches to solve this problem based on codon-shuffling have been limited to exploring only an infinitesimal fraction of the sequence space and by their use of parametric approximations. RESULTS: We present a novel O(N(log N)2)-time algorithm, CodingMotif, to identify nucleotide-level motifs of unusual copy number in protein-coding regions. Using a new dynamic programming algorithm we are able to exhaustively calculate the distribution of the number of occurrences of a motif over all possible coding sequences that encode the same amino acid sequence, given a background model for codon usage and dinucleotide biases. Our method takes advantage of the sparseness of loci where a given motif can occur, greatly speeding up the required convolution calculations. Knowledge of the distribution allows one to assess the exact non-parametric p-value of whether a given motif is over- or under- represented. We demonstrate that our method identifies known functional motifs more accurately than sampling and parametric-based approaches in a variety of coding datasets of various size, including ChIP-seq data for the transcription factors NRSF and GABP. CONCLUSIONS: CodingMotif provides a theoretically and empirically-demonstrated advance for the detection of motifs overrepresented in coding sequences. We expect CodingMotif to be useful for identifying motifs in functional genomic datasets such as DNA-protein binding, RNA-protein binding, or microRNA-RNA binding within coding regions. A software implementation is available at http://bioinformatics.bc.edu/chuanglab/codingmotif.tar.

There are thousands of strongly conserved non-coding elements (CNEs) in vertebrate genomes, and their functions remain largely unknown. However, without biologically relevant criteria for prioritizing them, selecting a particular CNE sequences to study can be haphazard. To address this problem, we present cneViewer-a database and webtool that systematizes information on conserved non-coding DNA elements in zebrafish. A key feature here is the ability to search for CNEs that may be relevant to tissue-specific gene regulation, based on known developmental expression patterns of nearby genes. cneViewer provides this and other organizing features that significantly facilitate experimental design and CNE analysis.