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1. Patterns of amino acid conservation in human and animal immunodeficiency viruses

   https://doi.org/10.1093/bioinformatics/btw441  

   Voitenko, Olga S. ; Dhroso, Andi ; Feldmann, Anna ; Korkin, Dmitry ; Kalinina, Olga V. ( August 2016 , Bioinformatics)

   Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity.

   We propose a method for identification of two types of surface residues clusters with abnormal conservation: extremely conserved and extremely variable clusters. We identify them on the surface of proteins from HIV and other animal immunodeficiency viruses. Both types of clusters are overrepresented on the interaction interfaces of viral proteins with other proteins, nucleic acids or low molecular-weight ligands, both in the viral particle and between the virus and its host. In the immunodeficiency viruses, the interaction interfaces are not more conserved than the corresponding proteins on an average, and we show that extremely conserved clusters coincide with protein–protein interaction hotspots, predicted as the residues with the largest energetic contribution to the interaction. Extremely variable clusters have been identified here for themore »first time. In the HIV-1 envelope protein gp120, they overlap with known antigenic sites. These antigenic sites also contain many residues from extremely conserved clusters, hence representing a unique interacting interface enriched both in extremely conserved and in extremely variable clusters of residues. This observation may have important implication for antiretroviral vaccine development.

   A Python package is available at https://bioinf.mpi-inf.mpg.de/publications/viral-ppi-pred/

   voitenko@mpi-inf.mpg.de or kalinina@mpi-inf.mpg.de

   Supplementary data are available at Bioinformatics online.

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2. Benchmarking of microbiome detection tools on RNA-seq synthetic databases according to diverse conditions

   https://doi.org/10.1093/bioadv/vbad014  

   Jurado-Rueda, Francisco ; Alonso-Guirado, Lola ; Perea-Cham-blee, Tomin E. ; Elliott, Oliver T. ; Filip, Ioan ; Rabadán, Raúl ; Malats, Núria ; Forslund, ed., Sofia ( February 2023 , Bioinformatics Advances)

   Here, we performed a benchmarking analysis of five tools for microbe sequence detection using transcriptomics data (Kraken2, MetaPhlAn2, PathSeq, DRAC and Pandora). We built a synthetic database mimicking real-world structure with tuned conditions accounting for microbe species prevalence, base calling quality and sequence length. Sensitivity and positive predictive value (PPV) parameters, as well as computational requirements, were used for tool ranking.

   GATK PathSeq showed the highest sensitivity on average and across all scenarios considered. However, the main drawback of this tool was its slowness. Kraken2 was the fastest tool and displayed the second-best sensitivity, though with large variance depending on the species to be classified. There was no significant difference for the other three algorithms sensitivity. The sensitivity of MetaPhlAn2 and Pandora was affected by sequence number and DRAC by sequence quality and length. Results from this study support the use of Kraken2 for routine microbiome profiling based on its competitive sensitivity and runtime performance. Nonetheless, we strongly endorse to complement it by combining with MetaPhlAn2 for thorough taxonomic analyses.

   https://github.com/fjuradorueda/MIME/ and https://github.com/lola4/DRAC/.

   Supplementary data are available at Bioinformatics Advances online.
3. PALADIN: protein alignment for functional profiling whole metagenome shotgun data

   https://doi.org/10.1093/bioinformatics/btx021  

   Westbrook, Anthony ; Ramsdell, Jordan ; Schuelke, Taruna ; Normington, Louisa ; Bergeron, R. Daniel ; Thomas, W. Kelley ; MacManes, Matthew D. ; Valencia, ed., Alfonso ( January 2017 , Bioinformatics)

   Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN—a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space.

   We compared the accuracy and efficiency of PALADIN against existing tools that employ nucleotide or protein alignment algorithms. Using simulated reads, PALADIN consistently outperformed the popular DNA read mappers BWA and NovoAlign in detected proteins, percentage of reads mapped and ontological similarity. We also compared PALADIN against four existing protein alignment tools: BLASTX, RAPSearch2, DIAMOND and Lambda, using empirically obtained reads. PALADIN yielded results seven times faster than the best performing alternative, DIAMOND and nearly 8000 times faster than BLASTX. PALADIN's accuracy was comparable to all tested solutions.

   PALADIN was implemented in C, and its source code and documentation are available at https://github.com/twestbrookunh/paladin

   Supplementary data are available at Bioinformatics online.
4. A data-adaptive Bayesian regression approach for polygenic risk prediction

   https://doi.org/10.1093/bioinformatics/btac024  

   Song, Shuang ; Hou, Lin ; Liu, Jun S. ; Schwartz, ed., Russell ( January 2022 , Bioinformatics)

   Polygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying genetic architectures and improves overall prediction accuracy for complex diseases by allowing for a wide class of prior choices. To take full advantage of the framework, we propose a summary-statistics-based cross-validation strategy to automatically select suitable chromosome-level priors, which demonstrates a striking variability of the prior preference of each chromosome, for the same complex disease, and further significantly improves the prediction accuracy.

   Simulation studies and real data applications with seven disease datasets from the Wellcome Trust Case Control Consortium cohort and eight groups of large-scale genome-wide association studies demonstrate that NeuPred achieves substantial and consistent improvements in terms of predictive r2 over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods.

   The R package implementing NeuPred is available at https://github.com/shuangsong0110/NeuPred.

   Supplementary data are available at Bioinformatics online.
5. DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates

   https://doi.org/10.1093/bioinformatics/btx357  

   Peng, Hao ; Yang, Yifan ; Zhe, Shandian ; Wang, Jian ; Gribskov, Michael ; Qi, Yuan ; Valencia, ed., Alfonso ( June 2017 , Bioinformatics)

   High-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy.

   We present a novel hierarchical Bayesian model called Differentially Expressed Isoform detection from Multiple biological replicates (DEIsoM) for identifying differentially expressed (DE) isoforms from multiple biological replicates representing two conditions, e.g. multiple samples from healthy and diseased subjects. DEIsoM first estimates isoform expression within each condition by (1) capturing common patterns from sample replicates while allowing individual differences, and (2) modeling the uncertainty introduced by ambiguous read mapping in each replicate. Specifically, we introduce a Dirichlet prior distribution to capture the common expression pattern of replicates from the same condition, and treat the isoform expression of individual replicates as samples from this distribution. Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assigned to the most probable isoform in each replicate. Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to improvemore »the accuracy and speed of identification of DE isoforms over alternative methods. Application of DEIsoM to an hepatocellular carcinoma (HCC) dataset identifies biologically relevant DE isoforms. The relevance of these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visualization, and the biological literature.

   The software is available at https://github.com/hao-peng/DEIsoM

   Supplementary data are available at Bioinformatics online.

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