PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.376871v1?rss=1

Authors: Wilks, C., Schatz, M. C.

Abstract:

Abstract Motivation: Long read sequencing has increased the accuracy and completeness of assemblies of various organisms' genomes in recent months. Similarly, spliced alignments of long read RNA sequencing hold the promise of delivering much longer transcripts of existing and novel isoforms in known genes without the need for error-prone transcript assemblies from short reads. However, low coverage and high-error rates potentially hamper the widespread adoption of long-read spliced alignments in annotation updates and isoform-level expression quantifications. Results: Addressing these issues, we first develop a simulation of error modes for both Oxford Nanopore and PacBio CCS spliced-alignments. Based on this we train a Random Forest classifier to assign new long-read alignments to one of two error categories, a novel category, or label them as non-error. We use this classifier to label reads from the spliced-alignments of the popular aligner minimap2, run on three long read sequencing datasets, including NA12878 from Oxford Nanopore and PacBio CCS, as well as a PacBio SKBR3 cancer cell line. Finally, we compare the intron chains of the three long read alignments against individual splice sites, short read assemblies, and the output from the FLAIR pipeline on the same samples. Our results demonstrate a substantial lack of precision in determining exact splice sites for long reads during alignment on both platforms while showing some benefit from postprocessing. This work motivates the need for both better aligners and additional post-alignment processing to adjust incorrectly called putative splice-sites and clarify novel transcripts support. Availability and implementation Source code for the random forest implemented in python is available at https://github.com/schatzlab/LongTron under the MIT license. The modified version of GffCompare used to construct Table 3 and related is here: https://github.com/ChristopherWilks/gffcompare/releases/tag/0.11.2LT

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.377572v1?rss=1

Authors: Olivieri, J. E., Dehghannasiri, R., Salzman, J.

Abstract:

To date, the field of single-cell genomics has viewed robust splicing analysis as completely out of reach in droplet-based platforms, preventing biological discovery of single-cell regulated splicing. Here, we introduce a novel, robust, and computationally efficient statistical method, the Splicing Z Score (SZS), to detect differential alternative splicing in single cell RNA-Seq technologies including 10x Chromium. We applied the SZS to primary human cells to discover new regulated, cell type-specific splicing patterns. Illustrating the power of the SZS method, splicing of a small set of genes has high predictive power for tissue compartment in the human lung, and the SZS identifies un-annotated, conserved splicing regulation in the human spermatogenesis. The SZS is a method that can rapidly identify regulated splicing events from single cell data and prioritize genes predicted to have functionally significant splicing programs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.377440v1?rss=1

Authors: O'Brien, J., Narayan, V., Wong, Y. L., Seitzer, P., Sandoval, C., Haste, N., Smith, M., Rad, R., Gaun, A., Baker, A., Kukurugya, M., Martin-McNulty, B., Zhang, C., Kolumam, G., Sidrauski, C., Jojic, V., McAllister, F., Bennett, B., Buffenstein, R.

Abstract:

Isotopic labeling with deuterium oxide (D2O) is a common technique for estimating in vivo protein turnover, but its use has been limited by two long-standing problems: (1) identifying non-monoisotopic peptides; and (2) estimating protein turnover rates in the presence of dynamic amino acid enrichment. In this paper, we present a novel experimental and analytical framework for solving these two problems. Peptides with high probabilities of labeling in many amino acids present fragmentation spectra that frequently do not match the theoretical spectra used in standard identification algorithms. We resolve this difficulty using a modified search algorithm we call Conditional Ion Distribution Search (CIDS). Increased identifications from CIDS along with direct measurement of amino acid enrichment and statistical modeling that accounts for heterogeneous information across peptides, dramatically improves the accuracy and precision of half-life estimates. We benchmark the approach in cells, where near-complete labeling is possible, and conduct an in vivo experiment revealing, for the first time, differences in protein turnover between mice and naked mole-rats commensurate with their disparate longevity.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.377689v1?rss=1

Authors: Hoogstrate, Y., Jenster, G., van de Werken, H. J.

Abstract:

Background: The FASTA file format used to store polymeric sequence data has become a bioinformatics file standard used for decades. The relatively large files require additional files beyond the scope of the original format, to identify sequences and provide random access. Currently, multiple compressors have been developed to archive FASTA files back and forth, but these lack direct access to targeted content or metadata of the archive. Moreover, these solutions are not directly backwards compatible to FASTA files, resulting in limited software integration. Results: We designed linux based a toolkit using Filesystem in Userspace (FUSE) that virtualises the content of DNA, RNA and protein FASTA archives into the filesystem. This guarantees in-sync virtualised metadata files and offers fast random-access decompression using Zstandard (zstd). The toolkit, FASTAFS, can track all system wide running instances, allows file integrity verification and can provide, instantly, scriptable access to sequence files and is easy to use and deploy. Conclusions: FASTAFS is a user-friendly and easy to deploy backwards compatible generic purpose solution to store and access compressed FASTA files, since it offers file system access to FASTA files as well as in-sync metadata files through file virtualisation. Using virtual filesystems as in-between layer offers the possibility to design format conversion without the need to rewrite code into different languages while preserving compatibility. Code Availability: https://github.com/yhoogstrate/fastafs .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.375717v1?rss=1

Authors: Tung, Y.-A., Yang, W.-T., Hsieh, T.-T., Chang, Y.-C., Wu, J.-T., Oyang, Y.-J., Chen, C.-Y.

Abstract:

Enhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet. Availability: The accuEnhancer package can be freely accessed at: https://github.com/callsobing/accuEnhancer

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.376988v1?rss=1

Authors: Balluet, M., Sizaire, F., Walter, T., Pont, J., Giroux, B., Bouchareb, O., Tramier, M., Pecreaux, J.

Abstract:

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy, during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced dataset due to cost and time to prepare the samples and have the datasets annotated by experts. We propose here a real-time image processing, compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning for the sake of understanding the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without any significant loss in accuracy, offering a substantial gain in execution time. We offer a method to select fast and discriminant features. In our assay, a 79.6 {+/-} 2.4% accurate classification of a cell took 68.7 {+/-} 3.5 ms (mean {+/-} SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into 8 phases of the cell cycle using 12 feature-groups and operating a consumer market ARM- based embedded system. Interestingly, a simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.377499v1?rss=1

Authors: Moshiri, N.

Abstract:

Motivation: The ability to simulate coalescent viral phylogenies constrained by a given transmission network can enable the benchmarking of computational tools used in molecular epidemiology as well as the ability to gain insights into unobservable aspects of the virology of a novel pathogen. However, such simulation experiments require generating a large number of technical simulation replicates, and existing tools for coalescent simulations along a transmission network are too slow to conduct such experiments at the scale of the global population. Results: CoaTran is a massively scalable tool that simulates a coalescent viral phylogeny constrained by a user-provided transmission network. CoaTran is written in highly-optimized C++ code and can generate global population scale phylogenetic coalescent simulations in seconds to minutes. Availability: CoaTran is freely available at https://github.com/niemasd/CoaTran as an open-source software project.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.376707v1?rss=1

Authors: Ulgen, E., Sezerman, U.

Abstract:

Cancer develops due to "driver" genomic alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomic data. However, methods for personalized analysis of cancer driver genes are underdeveloped. In this study, we developed a novel personalized and batch analysis approach for driver gene prediction utilizing somatic genomic data, called driveR. Combining somatic genomic information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that our approach performs adequately, outperforms existing approaches, and is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.10.376228v1?rss=1

Authors: Bartha, A., Gyorffy, B.

Abstract:

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation, and can serve as biomarkers of progression or as therapy targets with minimal off-target effects. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web-platform enabling mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays or RNA-seq. Gene array data were manually selected from NCBI-GEO. RNA sequencing data was downloaded from the TCGA, TARGET, and GTEx repositories. TCGA and TARGET contain predominantly tumor and metastatic samples from adult and pediatric patients, while GTEx samples are from healthy tissues. Statistical significance was computed using Mann-Whitney or Kruskall-Wallis tests. The entire database contains 56,938 samples including 33,520 samples from 3,180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3,691 normal samples), 11,010 samples from TCGA (394 metastatic, 9,886 tumorous and 730 normal), 1,193 samples from TARGET (1 metastatic, 1,180 tumor, 12 normal) and 11,215 normal samples from GTEx. The most consistently up-regulated genes across multiple tumor types were TOP2A (mean FC=7.8), SPP1 (FC=7.0) and CENPA (FC=6.03) and the most consistently down-regulated gene was ADH1B (mean FC=0.15). Validation of differential expression using equally sized training and test sets confirmed reliability of the database in breast, colon, and lung cancer (p<0.0001). The online analysis platform enables unrestricted mining of the database and is accessible at www.tnmplot.com.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.378802v1?rss=1

Authors: Lazeroff, M., Harris, S., Tsourkas, P.

Abstract:

The number of sequenced phage genomes is growing at an exponential rate. The majority of newly sequenced phage genomes are annotated by one or more of several freely-available auto-annotation programs. No program has been shown to consistently outperform the others; thus the choice of which program to use is not obvious. We present the software Phage Commander for rapid annotation of phage genomes using multiple auto-annotation programs. Phage Commander runs a phage genome sequence through nine auto-annotation programs and integrates the results within a single output. Phage Commander generates properly formatted output files for direct export to NCBI GenBank. Users can select the threshold for determining which genes should be exported. Phage Commander was benchmarked using eight high-quality phage genomes whose genes have been identified through experiments. Results show that the best results are obtained by exporting genes identified by at least two or three of the nine auto-annotation programs.

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