PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.321828v1?rss=1

Authors: Ekvall, M., Höhle, M., Käll, L.

Abstract:

Motivation: Permutation tests offer a straight forward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naive implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Results: Parallelization of the Green algorithm was found possible by nontrivial rearrangement of the structure of the algorithm. A speed-up - by orders of magnitude - is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g., the widely used asymptotic Mann-Whitney U-test. Availability: In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.320887v1?rss=1

Authors: Smit, J. H., Krishnamurthy, S., Srinivasu, B. Y., Karamanou, S., Economou, A.

Abstract:

Hydrogen Deuterium Exchange Mass Spectrometry (HDX-MS) is a powerful technique to monitor the intrinsic and conformational dynamics of proteins. Most HDX-MS experiments compare protein states (e.g. apoprotein vs liganded) and provide detailed information on differential dynamics between them obtained from multiple overlapping peptides. However, differential dynamics are difficult to compare across protein derivatives, oligomeric assemblies, homologues and samples treated under different buffer and protease conditions. A main reason is that peptide-based D-uptake differences do not inform on absolute intrinsic dynamics at the level of single aminoacyl residues. Such information is offered by protection factors, i.e. the position of the local equilibrium between the D-exchange-competent 'open' state and the non-exchanging 'closed' state. We present PyHDX, a software tool to calculate protection factors and Gibbs free energies typically within minutes from HDX-MS-derived peptide lists. PyHDX provides intrinsic information on the thermodynamics of protein dynamics at single-residue level. An interactive web interface further streamlines the process of transforming peptide lists to either coloured linear sequence maps or 3D structures of Gibbs free energies/protection factors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.320515v1?rss=1

Authors: Slim, L., Azencott, C.-A., Chatelain, C.

Abstract:

Association testing in genome-wide association studies (GWAS) is often performed at either the SNP level or the gene level. The two levels can bring different insights into disease mechanisms. In the present work, we provide a novel approach based on nonlinear post-selection inference to bridge the gap between them. Our approach selects, within a gene, the SNPs or LD blocks most associated with the phenotype, before testing their combined effect. Both the selection and the association testing are conducted nonlinearly. We apply our tool to the study of BMI and its variation in the UK BioBank. In this study, our approach outperformed other gene-level association testing tools, with the unique benefit of pinpointing the causal SNPs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.29.318907v1?rss=1

Authors: Olyaee, M. H., Khanteymoori, A., Khalifeh, K.

Abstract:

Decreasing the cost of high-throughput DNA sequencing technologies, provides a huge amount of data that enables researchers to determine haplotypes for diploid and polyploid organisms. Although various methods have been developed to reconstruct haplotypes in diploid form, their accuracy is still a challenging task. Also, most of the current methods cannot be applied to polyploid form. In this paper, an iterative method is proposed, which employs hypergraph to reconstruct haplotype. The proposed method by utilizing chaotic viewpoint can enhance the obtained haplotypes. For this purpose, a haplotype set was randomly generated as an initial estimate, and its consistency with the input fragments was described by constructing a weighted hypergraph. Partitioning the hypergraph specifies those positions in the haplotype set that need to be corrected. This procedure is repeated until no further improvement could be achieved. Each element of the finalized haplotype set is mapped to a line by chaos game representation, and a coordinate series is defined based on the position of mapped points. Then, some positions with low qualities can be assessed by applying a local projection. Experimental results on both simulated and real datasets demonstrate that this method outperforms most other approaches, and is promising to perform the haplotype assembly.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.29.319095v1?rss=1

Authors: Edgar, R. C.

Abstract:

Minimizers are widely used to select subsets of fixed-length substrings (k-mers) from biological sequences in applications ranging from read mapping to taxonomy prediction and indexing of large datasets. Syncmers are an alternative method for selecting a subset of k-mers. Unlike a minimizer, a syncmer is identified by its k-mer sequence alone and is therefore synchronized in the following sense: if a given k-mer is selected from one sequence, it will also be selected from any other sequence. Bounded syncmers are defined by a small and fast function of the k-mer sequence which exploits correlations between overlapping k-mers to guarantee that at least one syncmer must appear in a window of predetermined length, and therefore comprise a universal hitting set which does not require a precomputed lookup table. Bounded syncmers are shown to be unambiguously superior to minimizers because they achieve both lower density and better conservation in mutated sequences.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.29.318642v1?rss=1

Authors: Ke, Z., Vikalo, H.

Abstract:

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data. High-throughput sequencing technologies generate enormous amounts of short fragments (reads) which essentially oversample components of a mixture; the representation redundancy enables reconstruction of the components (haplotypes, viral strains). The reconstruction problem, known to be NP-hard, boils down to grouping together reads originating from the same component in a mixture. Existing methods struggle to solve this problem with required level of accuracy and low runtimes; the problem is becoming increasingly more challenging as the number and length of the components increase. This paper proposes a read clustering method based on a convolutional auto-encoder designed to first project sequenced fragments to a low-dimensional space and then estimate the probability of the read origin using learned embedded features. The components are reconstructed by finding consensus sequences that agglomerate reads from the same origin. Mini-batch stochastic gradient descent and dimension reduction of reads allow the proposed method to efficiently deal with massive numbers of long reads. Experiments on simulated, semi-experimental and experimental data demonstrate the ability of the proposed method to accurately reconstruct haplotypes and viral quasispecies, often demonstrating superior performance compared to state-of-the-art methods.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.30.319921v1?rss=1

Authors: Shiga, M., Seno, S., Onizuka, M., Matsuda, H.

Abstract:

Unsupervised cell clustering is important in discovering cell diversity and subpopulations. Single-cell clustering using gene expression profiles is known to show different results depending on the method of expression quantification; nevertheless, most single-cell clustering methods do not consider the method. In this article, we propose a robust and highly accurate clustering method using joint non-negative matrix factorization (joint NMF) based on multiple gene expression profiles quantified using different methods. Matrix factorization is an excellent method for dimension reduction and feature extraction of data. In particular, NMF approximates the data matrix as the product of two matrices in which all factors are non-negative. Our joint NMF can extract common factors among multiple gene expression profiles by applying each NMF to them under the constraint that one of the factorized matrices is shared among the multiple NMFs. The joint NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to the conventional clustering methods, which uses only a single quantification method. In conclusion, our study showed that our clustering method using multiple gene expression profiles is more accurate than other popular methods.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.01.321950v1?rss=1

Authors: Leeuwenburgh, V. C., Urzua-Traslavina, C. G., Bhattacharya, A., Walvoort, M. T. C., Jalving, M., de Jong, S., Fehrmann, R. S. N.

Abstract:

Patient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. However, these bulk profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in the biopsy. Therefore, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. We therefore performed consensus Independent Component Analyses (c-ICA) with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues and cell lines. c-ICA enabled us to create a transcriptional metabolic landscape in which many robust metabolic transcriptional components (mTCs) and their activation score in individual samples were defined. Here we demonstrate that this metabolic landscape can be used to explore associations between metabolic processes and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. The metabolic landscape can be explored at http://www.themetaboliclandscapeofcancer.com.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.29.317305v1?rss=1

Authors: Sanchez, J. A., Gil-Martinez, A. L., Cisterna, A., Garcia-Ruiz, S., Gomez, A., Reynolds, R. H., Nalls, M. A., Hardy, J., Ryten, M., Botia, J. A.

Abstract:

Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. The tool is implemented in R and available at GitHub,https://github.com/drlaguna/GMSCA as open source software.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.317594v1?rss=1

Authors: Baharlouei, S., Razaviyayn, M., Tseng, E., Tse, D.

Abstract:

Long-read sequencing technologies demonstrate high potential for de novo discovery of complex transcript isoforms, but high error rates pose a significant challenge. Existing error correction methods rely on clustering reads based on isoform-level alignment and cannot be efficiently scaled. We propose a new method, I-CONVEX, that performs fast, alignment-free isoform clustering with almost linear computational complexity, and leads to better consensus accuracy on simulated, synthetic, and real datasets.

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