PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.18.388504v1?rss=1

Authors: Koch, H., Keller, C. A., Giardine, B., Xiang, G., Zhang, F., Wang, Y., Hardison, R. C., Li, Q.

Abstract:

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. Our approach improves upon existing methods by boosting statistical power to identify meaningful signals while retaining interpretability and computational tractability. We illustrate CLIMB's value on two sets of hematopoietic data: one studying CTCF ChIP-seq measured in 17 different cell populations, and another examining RNA-seq measured across constituent cell populations in three committed lineages. These analyses demonstrate that CLIMB captures biologically relevant clusters in the data and improves upon commonly-used pairwise comparisons and unsupervised clusterings typical of genomic analyses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.385633v1?rss=1

Authors: Chen, L., Jiang, Y., Yao, B., Huang, K., Liu, Y., Wang, Y., Qin, X., Saykin, A. J., Wang, Y.

Abstract:

Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies (GWAS) or quantitative trait locus (QTL) analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for for prioritizing non- coding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first "context-free" mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict noncoding variants of unknown and diverse context. The second "context-dependent" mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.17.387779v1?rss=1

Authors: Song, D., Li, J. J.

Abstract:

In the investigation of molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along a continuous cell trajectory, which can be estimated by pseudotime inference from single-cell RNA-sequencing (scRNA-seq) data. However, existing methods that identify DE genes based on inferred pseudotime do not account for the uncertainty in pseudotime inference. Also, they either have ill-posed p-values that hinder the control of false discovery rate (FDR) or have restrictive models that reduce the power of DE gene identification. To overcome these drawbacks, we propose PseudotimeDE, a robust method that accounts for the uncertainty in pseudotime inference and thus identifies DE genes along cell pseudotime with well-calibrated p-values. PseudotimeDE is flexible in allowing users to specify the pseudotime inference method and to choose the appropriate model for scRNA-seq data. Comprehensive simulations and real-data applications verify that PseudotimeDE provides well-calibrated p-values essential for controlling FDR and downstream analysis and that PseudotimeDE is more powerful than existing methods to identify DE genes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.384834v1?rss=1

Authors: Verschaffelt, P., Van Den Bossche, T., Gabriel, W., Burdukiewicz, M., Soggiu, A., Martens, L., Renard, B. Y., Schiebenhoefer, H., Mesuere, B.

Abstract:

The study of microbiomes has gained in importance over the past few years, and has led to the fields of metagenomics, metatranscriptomics and metaproteomics. While initially focused on the study of biodiversity within these communities the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute functional similarity between two data sets. MegaGO is highly performant: each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license, and is available at https://github.com/MEGA-GO/.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.384479v1?rss=1

Authors: Vuong, H., Truong, T., Phan, T., Pham, S.

Abstract:

Most widely used tools for finding marker genes in single-cell data (SeuratT/NegBinom/Poisson, CellRanger, EdgeR, limmatrend) use a conventional definition of differentially expressed genes: genes with different mean expression values. However, in single-cell data, a cell population can be a mixture of many cell types/cell states, hence the mean expression of genes cannot represent the whole population. In addition, these tools assume that the gene expression of a population belongs to a specific family of distribution. This assumption is often violated in single-cell data. In this work, we define marker genes of a cell population as genes that can be used to distinguish cells in the population from cells in other populations. Besides log-fold change, we devise a new metric to classify genes into up-regulated, down-regulated, and transitional states. In a benchmark for finding up-regulated and down-regulated genes, our tool outperforms all compared methods, including Seurat, ROTS, scDD, edgeR, MAST, limma, normal t test, Wilcoxon and KolmogorovSmirnov test. Our method is much faster than all compared methods, therefore, enables interactive analysis for large single cell data sets in BioTuring Browser. Venice algorithm is available within Signac package: https://github.com/bioturing/signac

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.15.383448v1?rss=1

Authors: Lin, F. P.

Abstract:

BACKGROUND: The advances in genome sequencing technologies have provided new opportunities for delivering targeted therapy to patients with advanced cancer. However, these high-throughput assays have also created a multitude of challenges for oncologists in treatment selection, demanding a new approach to support decision-making in clinics. METHODS: To address this unmet need, this paper describes the design of a symbolic reasoning framework using the method of hierarchical task analysis. Based on this framework, an evidence-based treatment recommendation system was implemented for supporting decision-making based on a patient's clinicopathologic and biomarker profiles. RESULTS: This intelligent framework captures a six-step sequential decision process: (1) concept expansion by ontology matching, (2) evidence matching, (3) evidence grading and value-based prioritisation, (4) clinical hypothesis generation, (5) recommendation ranking, and (6) recommendation filtering. The importance of balancing evidence-based and hypothesis-driven treatment recommendations is also highlighted. Of note, tracking history of inference has emerged to be a critical step to allow rational prioritisation of recommendations. The concept of inference tracking also enables the derivation of a novel measure -- level of matching -- that helps to convey whether a treatment recommendation is drawn from incomplete knowledge during the reasoning process. CONCLUSIONS: This framework systematically encapsulates oncologist's treatment decision-making process. Further evaluations in prospective clinical studies are warranted to demonstrate how this computational pipeline can be integrated into oncology practice to improve outcomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.15.363259v1?rss=1

Authors: Leonard, R. R., Leleu, M., Van Vlierberghe, M., Kerff, F., BAURAIN, D.

Abstract:

TQMD is a tool which downloads, stores and produces lists of dereplicated prokaryotic genomes. It has been developed to counter the ever-growing number of prokaryotic genomes and their uneven taxonomic distribution. It is based on word-based alignment-free methods (k-mers), an iterative single-linkage approach and a divide-and-conquer strategy to remain both efficient and scalable. We studied the performance of TQMD by verifying the influence of its parameters and heuristics on the clustering outcome. We further compared TQMD to two other dereplication tools (dRep and Assembly-Dereplicator). Our results showed that TQMD is optimized to dereplicate at high taxonomic levels (phylum/class), whereas the other dereplication tools are optimized for lower taxonomic levels (species/strain), making TQMD complementary to the existing dereplicating tools. TQMD is available at [https://bitbucket.org/phylogeno/tqmd].

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.15.383273v1?rss=1

Authors: Ren, J., Chaisson, M.

Abstract:

It is computationally challenging to detect variation by aligning long reads from single-molecule sequencing (SMS) instruments, or megabase-scale contigs from SMS assemblies. One approach to efficiently align long sequences is sparse dynamic programming (SDP), where exact matches are found between the sequence and the genome, and optimal chains of matches are found representing a rough alignment. Sequence variation is more accurately modeled when alignments are scored with a gap penalty that is a convex function of the gap length. Because previous implementations of SDP used a linear-cost gap function that does not accurately model variation, and implementations of alignment that have a convex gap penalty are either inefficient or use heuristics, we developed a method, lra, that uses SDP with a convex-cost gap penalty. We use lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments as well as de novo assembly contigs. Across all data types, the runtime of lra is between 52-168% of the state of the art aligner minimap2 when generating SAM alignment, and 9-15% of an alternative method, ngmlr. This alignment approach may be used to provide additional evidence of SV calls in PacBio datasets, and an increase in sensitivity and specificity on ONT data with current SV detection algorithms. The number of calls discovered using pbsv with lra alignments are within 98.3-98.6% of calls made from minimap2 alignments on the same data, and give a nominal 0.2-0.4% increase in F1 score by Truvari analysis. On ONT data with SV called using Sniffles, the number of calls made from lra alignments is 3% greater than minimap2-based calls, and 30% greater than ngmlr based calls, with a 4.6-5.5% increase in Truvari F1 score. When applied to calling variation from de novo assembly contigs, there is a 5.8% increase in SV calls compared to minimap2+paftools, with a 4.3% increase in Truvari F1 score.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.15.383661v1?rss=1

Authors: Kuchroo, M., Huang, J., Wong, P., Grenier, J.-C., Shung, D., Tong, A., Lucas, C., Klein, J., Burkhardt, D., Gigante, S., Godavarthi, A., Israelow, B., Mao, T., Oh, J. E., Silva, J., Takahashi, T., Odio, C. D., Casanovas-Massana, A., Fournier, J., IMPACT Team, Y., Farhadian, S., Dela Cruz, C. S., Ko, A. I., Wilson, F. P., Hussin, J., Wolf, G., Iwasaki, A., Krishnaswamy, S.

Abstract:

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66lo neutrophil and IFN{gamma}+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.17.387068v1?rss=1

Authors: Malhotra, S., Joseph, A. P., Thiyagalingam, J., Topf, M.

Abstract:

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for the map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we have assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed PI-score, a density-independent machine learning-based metric, trained using protein-protein interfaces features in high-resolution crystal structures. Using PI-score, we were able to identify errors at interfaces in the PDB-deposited cryo-EM structures (including SARS-CoV-2 complexes) and in the models submitted for cryo-EM targets in CASP13 and the EM model challenge. Some of the identified errors, especially at medium-to-low resolution structures, were not captured by density-based assessment scores. Our method can therefore provide a powerful complementary assessment tool for the increasing number of complexes solved by cryo-EM.

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