SPAdes-St. Petersburg genome Assembler-was originally developed for de novo assembly of genome sequencing data produced for cultivated microbial isolates and for single-cell genomic DNA sequencing. With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole-genome sequencing and metagenomic datasets. In addition, we present guidelines for understanding results with use cases for each pipeline, and several additional support protocols that help in using SPAdes properly. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Assembling isolate bacterial datasets Basic Protocol 2: Assembling metagenomic datasets Basic Protocol 3: Assembling sets of putative plasmids Basic Protocol 4: Assembling transcriptomes Basic Protocol 5: Assembling putative biosynthetic gene clusters Support Protocol 1: Installing SPAdes Support Protocol 2: Providing input via command line Support Protocol 3: Providing input data via YAML format Support Protocol 4: Restarting previous run Support Protocol 5: Determining strand-specificity of RNA-seq data.

Cover: In Noor et al. (http://doi.org/10.1002/cpbi.101), the image shows overview of quality check plots from DIA report files.

Cover: In Mias and Zheng (http://doi.org/10.1002/cpbi.91), the image that using FilterMissing the authors remove data with less than ¾ of the points available (i.e., time series for which more than ¼ of the points are missing are removed).

We present Mothulity-a novel interface for Mothur, a well-established tool for 16S/ITS biodiversity analysis. Although Mothur is a well-documented and virtually complete software suite, its proper execution might be challenging for first-time users, and editing the Mothur batch scripts is time consuming even for experienced users. Mothur produces little to no graphical output, leaving the generation of plots to the user. Mothulity minimizes the chance of human error through a minimalistic yet powerful interface, with most of the analysis parameters predefined or adjusted automatically. Time spent on running the analysis is drastically reduced, since Mothulity produces an HTML report with publication-quality figures. Finally, Mothulity can be conveniently used with the SLURM workload manager, and is thereby suitable for a range of computing facilities. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Standard operational procedure (SOP) Basic Protocol 2: Generating report on pre-processed data.

The three-dimensional (3D) interactions of chromatin regulate cell-type-specific gene expression, recombination, X-chromosome inactivation, and many other genomic processes. High-throughput chromatin conformation capture (Hi-C) technologies capture the structure of the chromatin on a global scale by measuring all-vs.-all interactions and can provide new insights into genomic regulation. The workflow presented here describes how to analyze and interpret a comparative Hi-C experiment. We describe the process of obtaining Hi-C data from public repositories and give suggestions for pre-processing pipelines for users who intend to analyze their own raw data. We then describe the data normalization and comparative analysis process. We present three protocols describing the use of the multiHiCcompare, diffHic, and FIND R packages, respectively, to perform a comparative analysis of Hi-C experiments. Finally, visualization of the results and downstream interpretation of the differentially interacting regions are discussed. The bulk of this tutorial uses the R programming environment, and the processes described can be performed with most operating systems and a single computer. © 2019 by John Wiley & Sons, Inc.

Rfam is a database of non-coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature-based curation and a custom software pipeline, Rfam converts descriptions of RNA families found in the scientific literature into computational models that can be used to annotate RNAs belonging to those families in any DNA or RNA sequence. Valuable research outputs that are often locked up in figures and supplementary information files are encapsulated in Rfam entries and made accessible through the Rfam Web site. The data produced by Rfam have a broad application, from genome annotation to providing training sets for algorithm development. This article gives an overview of how to search and navigate the Rfam Web site, and how to annotate sequences with RNA families. The Rfam database is freely available at http://rfam.org. © 2018 by John Wiley & Sons, Inc.

MetaboSignal is an R/Bioconductor package designed to explore the relationships between genes and metabolites, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) as its primary database. It is a network-based approach that allows overlaying metabolic and signaling pathways and exploring the topological relationship between genes (signaling or metabolic genes) and metabolites. MetaboSignal is ideally suited to identify candidate genes in metabolome genome-wide association studies (mGWAS), particularly in the case of trans-acting associations. It can also be used to provide mechanistic explanations of perturbed metabolic patterns observed in genetic models, as well as to identify novel target metabolic pathways of signaling genes. © 2018 by John Wiley & Sons, Inc.

With the rapid growth in the number of solved protein structures stored in the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB), it is essential to develop tools to perform real-time structure similarity searches against the entire structure database. Since conventional structure alignment methods need to sample different orientations of proteins in the three-dimensional space, they are time consuming and unsuitable for rapid, real-time database searches. To this end, we have developed 3D-SURFER and EM-SURFER, which utilize 3D Zernike descriptors (3DZD) to conduct high-throughput protein structure comparison, visualization, and analysis. Taking an atomic structure or an electron microscopy map of a protein or a protein complex as input, the 3DZD of a query protein is computed and compared with the 3DZD of all other proteins in PDB or EMDB. In addition, local geometrical characteristics of a query protein can be analyzed using VisGrid and LIGSITECSC in 3D-SURFER. This article describes how to use 3D-SURFER and EM-SURFER to carry out protein surface shape similarity searches, local geometric feature analysis, and interpretation of the search results. © 2017 by John Wiley & Sons, Inc.