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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Improving the performance of minimizers andwinnowing schemes

The minimizers scheme is a method for selecting k-mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g., too many k-mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k-mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.\n\nWe provide an in-depth analysis of the effect of k-mer ordering on the performance of the minimizers technique. By using small universal hitting sets (a recently defined concept), we show how to significantly improve the performance of minimizers and avoid some of its worse behaviors. Based on these results, we encourage bioinformatics software developers to use an ordering based on a universal hitting set or, if not possible, a randomized ordering, rather than the lexicographic order. This analysis also settles negatively a conjecture (by Schleimer et al.) on the expected density of minimizers in a random sequence.\n\nThe software used for this analysis is available on GitHub: https://github.com/gmarcais/minimizers.git.\n\nContact: gmarcais@cs.cmu.edu

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bioRxiv2017

GenomicDataCommons: a Bioconductor Interface to the NCI Genomic Data Commons

The National Cancer Institute (NCI) Genomic Data Commons (Grossman et al. 2016, https://gdc.cancer.gov/) provides the cancer research community with an open and unified repository for sharing and accessing data across numerous cancer studies and projects via a high-performance data transfer and query infrastructure. The Bioconductor project (Huber et al. 2015) is an open source and open development software project built on the R statistical programming environment (R Core Team 2016). A major goal of the Bioconductor project is to facilitate the use, analysis, and comprehension of genomic data. The GenomicDataCommons Bioconductor package provides basic infrastructure for querying, accessing, and mining genomic datasets available from the GDC. We expect that Bioconductor developer and bioinformatics community will build on the GenomicDataCommons package to add higher-level functionality and expose cancer genomics data to many state-of-the-art bioinformatics methods available in Bioconductor.\n\nAvailabilityhttps://github.com/Bioconductor/GenomicDataCommons (& soon in Bioconductor).

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bioRxiv2017

PinAPL-Py: a web-service for the analysis of CRISPR-Cas9 Screens

BackgroundLarge-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise.\n\nResultsTo make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots.\n\nConclusionsPinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions, documentation and test datasets. The source code is available at https://github.com/LewisLabUCSD/PinAPL-Py

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bioRxiv2017

iDEP: An integrated web application for differential expression and pathway analysis

The analysis and interpretation of the RNA-Seq data can be time-consuming and challenging. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 208 annotation databases for plant and animal species, and 2 web services. The workflow can be reproduced by downloading customized R code and related files. As demonstrated by two examples, iDEP (http://ge-lab.org/idep/) democratizes access to bioinformatics resources and empowers biologists to easily gain actionable insights from transcriptomic data.

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bioRxiv2017

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

BackgroundThe advent of next-generation sequencing technologies has opened new avenues for clinical genomics research. In particular, as sequencing costs continue to decrease, an ever-growing number of clinical genomics institutes now rely on DNA sequencing studies at varying scales - genome, exome, mendeliome - for uncovering disease-associated variants or genes, in both rare and non-rare diseases.\n\nA common methodology for identifying such variants or genes is to rely on genetic association studies (GAS), that test whether allele or genotype frequencies differ between two groups of individuals, usually diseased subjects and healthy controls. Current bioinformatics tools for performing GAS are designed to run on standalone machines, and do not scale well with the increasing size of study designs and the search for multi-locus genetic associations. More efficient distributed and scalable data analysis solutions are needed to address this challenge.\n\nResultsWe developed a Big Data solution stack for distributing computations in genetic association studies, that address both single and multi-locus associations. The proposed stack, called DiGeST (Distributed Gene/variant Scoring Tool) is divided in two main components: a Hadoop/Spark high-performance computing back-end for efficient data storage and distributed computing, and a Web front-end providing users with a rich set of options to filter, compare and explore exome data from different sample populations. Using exome data from the 1000 Genomes Project, we show that our distributed implementation smoothly scales with computing resources. We make the resulting software stack Open-Source, and provide virtualisation scripts to run the complete environment both on standalone machine or Hadoop-based cluster.\n\nConclusionsHadoop/Spark provides a powerful and well-suited distributed computing framework for genetic association studies. Our work illustrates the flexibility, ease of use and scalability of the framework, and more generally advocates for its wider adoption in bioinformatics pipelines.

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bioRxiv2017

OligoMiner: A rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes

Oligonucleotide (oligo)-based fluorescence in situ hybridization (FISH) has emerged as an important tool for the study of chromosome organization and gene expression and has been empowered by the commercial availability of highly complex pools of oligos. However, a dedicated bioinformatic design utility has yet to be created specifically for the purpose of identifying optimal oligo FISH probe sequences on the genome-wide scale. Here, we introduce OligoMiner, a rapid and robust computational pipeline for the genome-scale design of oligo FISH probes that affords the scientist exact control over the parameters of each probe. Our streamlined method uses standard bioinformatic file formats, allowing users to seamlessly integrate existing and new utilities into the pipeline as desired, and introduces a novel method for evaluating the specificity of each probe molecule that connects simulated hybridization energetics to rapidly generated sequence alignments using supervised learning. We demonstrate the scalability of our approach by performing genome-scale probe discovery in numerous model organism genomes and showcase the performance of the resulting probes with both diffraction-limited and single-molecule super-resolution imaging of chromosomal and RNA targets. We anticipate this pipeline will make the FISH probe design process much more accessible and will more broadly facilitate the design of pools of hybridization probes for a variety of applications.

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bioRxiv2017

ASaiM: a Galaxy-based framework to analyze raw shotgun data from microbiota

BackgroundNew generation of sequencing platforms coupled to numerous bioinformatics tools has led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies.\n\nFindingsWe therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides a curated collection of tools to explore and visualize taxonomic and functional information from raw amplicon, metagenomic or metatranscriptomic sequences. To guide different analyses, several customizable workflows are included. All workflows are supported by tutorials and Galaxy interactive tours to guide the users through the analyses step by step. ASaiM is implemented as Galaxy Docker flavour. It is scalable to many thousand datasets, but also can be used a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io/)\n\nConclusionsBased on the Galaxy framework, ASaiM offers sophisticated analyses to scientists without command-line knowledge. ASaiM provides a powerful framework to easily and quickly explore microbiota data in a reproducible and transparent environment.

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bioRxiv2019

Homoeologous gene expression and co-expression network analyses and evolutionary inference in allopolyploids

Polyploidy is a widespread phenomenon throughout eukaryotes. Due to the coexistence of duplicated genomes, polyploids offer unique challenges for estimating gene expression levels, which is essential for understanding the massive and various forms of transcriptomic responses accompanying polyploidy. Although previous studies have explored the bioinformatics of polyploid transcriptomic profiling, the causes and consequences of inaccurate quantification of transcripts from duplicated gene copies have not been addressed. Using transcriptomic data from the cotton genus (Gossypium) as an example, we present an analytical workflow to evaluate a variety of bioinformatic method choices at different stages of RNA-seq analysis, from homoeolog expression quantification to downstream analysis used to infer key phenomena of polyploid expression evolution. In general, GSNAP-PolyCat outperforms other quantification pipelines tested, and its derived expression dataset best represents the expected homoeolog expression and co-expression divergence. The performance of co-expression network analysis was less affected by homoeolog quantification than by network construction methods, where weighted networks outperformed binary networks. By examining the extent and consequences of homoeolog read ambiguity, we illuminate the potential artifacts that may affect our understanding of duplicate gene expression, including an over-estimation of homoeolog co-regulation and the incorrect inference of subgenome asymmetry in network topology. Taken together, our work points to a set of reasonable practices that we hope are broadly applicable to the evolutionary exploration of polyploids.

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bioRxiv2019

Chemical map-based prediction of nucleosome positioning using the Bioconductor package nuCpos

BackgroundAssessing the nucleosome-forming potential of specific DNA sequences is important for understanding complex chromatin organization. Methods for predicting nucleosome positioning include bioinformatics and biophysical approaches. An advantage of bioinformatics methods, which are based on in vivo nucleosome maps, is the use of natural sequences that may contain previously unknown elements involved in nucleosome positioning in vivo. The accuracy of such prediction attempts reflects the genomic coordinate resolution of the nucleosome maps applied. Nucleosome maps are constructed using micrococcal nuclease digestion followed by high-throughput sequencing (MNase-seq). However, as MNase has a strong preference for A/T-rich sequences, MNase-seq may not be appropriate for this purpose. In addition to MNase-seq-based maps, base pair-resolution chemical maps of in vivo nucleosomes from three different species (budding and fission yeasts, and mice) are currently available. However, these chemical maps have yet to be integrated into publicly available computational methods. ResultsWe developed a Bioconductor package (named nuCpos) to demonstrate the superiority of chemical maps in predicting nucleosome positioning. The accuracy of chemical map-based prediction in rotational settings was higher than that of the previously developed MNase-seq-based approach. With our method, predicted nucleosome occupancy reasonably matched in vivo observations and was not affected by A/T nucleotide frequency. Effects of genetic alterations on nucleosome positioning that had been observed in living yeast cells could also be predicted. nuCpos calculates individual histone binding affinity (HBA) scores for given 147-bp sequences to examine their suitability for nucleosome formation. We also established local HBA as a new parameter to predict nucleosome formation, which was calculated for 13 overlapping nucleosomal DNA subsequences. HBA and local HBA scores for various sequences agreed well with previous in vitro and in vivo studies. Furthermore, our results suggest that nucleosomal subsegments that are disfavored in different rotational settings contribute to the defined positioning of nucleosomes. ConclusionsOur results demonstrate that chemical map-based statistical models are beneficial for studying nucleosomal DNA features. Studies employing nuCpos software can enhance understanding of chromatin regulation and the interpretation of genetic alterations and facilitate the design of artificial sequences.

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bioRxiv2020

BEE : a web service for biomedical entity exploration

Recently there has been a trend in bioinformatics to produce and manage large quantities of data to better explain complex life phenomena through relationship and interactions among biomedical entities. This increase in data leads to a need for more efficient management and searching capabilities. As a result, Semantic Web technologies have been applied to biomedical data. To use these technologies, users have to learn a query language such as SPARQL in order to ask complex queries such as What are the drugs associated with the disease breast carcinoma and Osteoporosis but not the gene ESR1. BEE was developed to overcome the limitations and difficulties of learning such query languages. Our proposed system provides an intuitive and effective query interface based on natural language. Our system is a heterogeneous biomedical entity query system based on pathway, drug, microRNA, disease and gene datasets from DGIdb, Tarbase, Human Phenotype Ontology and Reactome, Gene Ontology, KEGG gene set of MSigDB. User queries can be joined with union, intersection and negation operators. The system also allows for selected results to be saved and later combined with newly created queries. To the best of our knowledge, BEE is the first system that supports condition search based on the relationship of heterogeneous biomedical entities and is expected to be used in various fields of bioinformatics such as in drug repositioning candidate selection as well as simple knowledge search.

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bioRxiv2020

NetCombin: An algorithm for optimal level-k network construction from triplets

Phylogenetic networks construction is one the most important challenge in phylogenetics. These networks can present complex non-treelike events such as gene flow, horizontal gene transfers, recombination or hybridizations. Among phylogenetic networks, rooted structures are commonly used to represent the evolutionary history of a species set, explicitly. Triplets are well known input for constructing the rooted networks. Obtaining an optimal rooted network that contains all given triplets is main problem in network construction. The optimality criteria include minimizing the level and the number of reticulation nodes. The complexity of this problem is known to be NP-hard. In this research, a new algorithm called Netcombin is introduced to construct an optimal network which is consistent with input triplets. The innovation of this algorithm is based on binarization and expanding processes. The binarization process innovatively uses a measure to construct a binary rooted tree T consistent with the maximum number of input triplets. Then T is expanded in an intellectual process by adding minimum number of edges to obtain final network with the minimum number of reticulation nodes. In order to evaluate the proposed algorithm, NetCombin is compared with four state of the art algorithms, RPNCH, NCHB, TripNet, and SIMPLISTIC. The experimental results on real data indicate that by considering the trade-off between speed and precision, the NetCombin outperforms the others. Author summaryHadi Poormohammadi got his PhD in Mathematics, Applied combinatorics from Shahid Beheshti University, Tehran, Iran in 2013. He is now working as an assistant professor at the Faculty of Computer Engineering, Meybod University. His research interests include Combinatorics, Graph theory and Bioinformatics. Mohsen Sardari Zarchi got his PhD in computer engineering, Artificial Intelligence from University of Isfahan in 2015. He is now working as an assistant professor at the Faculty of Computer Engineering, Meybod University. His research interests include Deep learning, Image processing, Artificial intelligence and Bioinformatics.

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bioRxiv2020

Inferring Disease-Associated Piwi-Interacting RNAs via Graph Attention Networks

MotivationPIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlates with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes out gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. This study aims to make preliminary attempts on bionetwork based graph mining. ResultsIn this study, we present a method based on graph attention network to identify potential and biologically significant piRNA-disease associations (PDAs), called GAPDA. The attention mechanism can calculate a hidden representation of an association in the network based on neighbor nodes and assign weights to the input to make decisions. In particular, we introduced the attention-based Graph Neural Networks to the field of bio-association prediction for the first time, and proposed an abstract network topology suitable for small samples. Specifically, we combined piRNA sequence information and disease semantic similarity with piRNA-disease association network to construct a new attribute network. In the experiment, GAPDA performed excellently in five-fold cross-validation with the AUC of 0.9038. Not only that, but it still has superior performance compared to methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research. Contactzhuhongyou@ms.xjb.ac.cn;leiwang@ms.xjb.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2020

EnhancerP-2L: A Gene regulatory site identification tool for DNA enhancer region using CREs motifs

Enhancers are DNA fragments that do not encode RNA molecules and proteins, but they act critically in the production of RNAs and proteins by controlling gene expression. Prediction of enhancers and their strength plays significant role in regulating gene expression. Prediction of enhancer regions, in sequences of DNA, is considered a difficult task due to the fact that they are not close to the target gene, have less common motifs and are mostly tissue/cell specific. In recent past, several bioinformatics tools were developed to discriminate enhancers from other regulatory elements and to identify their strengths as well. However the need for improvement in the quality of its prediction method requires enhancements in its application value practically. In this study, we proposed a new method that builds on nucleotide composition and statistical moment based features to distinguish between enhancers and non-enhancers and additionally determine their strength. Our proposed method achieved accuracy better than current state-of-the-art methods using 5-fold and 10-fold cross-validation. The outcomes from our proposed method suggest that the use of statistical moments based features could bear more efficient and effective results. For the accessibility of the scientific community, we have developed a user-friendly web server for EnhancerP-2L which will increase the impact of bioinformatics on medicinal chemistry and drive medical science into an unprecedented resolution. Web server is freely accessible at http://www.biopred.org/enpred.

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bioRxiv2020

rG4-seeker enables high-confidence identification of novel and non-canonical rG4 motifs from rG4-seq experiments

We recently developed the rG4-seq method to detect and map in vitro RNA G-quadruplex (rG4s) structures on a transcriptome-wide scale. rG4-seq of purified human HeLa RNA has revealed many non-canonical rG4s and the effects adjacent sequences have on rG4 formation. In this study, we aimed to improve the outcomes and false-positive discrimination in rG4-seq experiments using a bioinformatic approach. By establishing connections between rG4-seq library preparation chemistry and the underlying properties of sequencing data, we identified how to mitigate indigenous sampling errors and background noise in rG4-seq. We applied these findings to develop a novel bioinformatics pipeline named rG4-seeker(https://github.com/TF-Chan-Lab/rG4-seeker), which uses tailored noise models to autonomously assess and optimize rG4 detections in a replicate-independent manner. Compared with previous methods, rG4-seeker exhibited better false-positive discrimination and improved sensitivity for non-canonical rG4s. Using rG4-seeker, we identified novel features in rG4 formation that were missed previously. rG4-seeker provides a reliable and sensitive approach for rG4-seq investigations, laying the foundations for further elucidation of rG4 biology.

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bioRxiv2020

DeepTracer: Predicting Backbone Atomic Structure from High Resolution Cryo-EM Density Maps of Protein Complexes

MotivationAccurately determining the atomic structure of proteins represents a fundamental problem in the field of structural bioinformatics. A solution would be significant as protein structure information could be utilized in the medical field, e.g. in the development of vaccines for new viruses. This paper focuses on predicting the protein structure based on 3D images of the proteins captured through cryogenic electron microscopes (cryo-EM). A fully automated computationally efficient protein structure prediction method would be particularly beneficial in the field of cryo-EM as the technology allows researchers to photograph multiple large protein complexes in a single study, which means that a fast prediction method could allow for a high throughput of derived protein structures. We present a deep learning approach, DeepTracer, for predicting locations of the backbone atoms, secondary structure elements, and the amino acid types. In order to connect the predicted amino acids into chains, we applied a modified traveling salesman algorithm. ResultsWe trained our deep learning model on experimental cryo-EM density maps and tested it on a set of 50 density maps. We found that our new approach predicted protein structures with an average RMSD value of 1.18 and a coverage of 87.5%. Furthermore, we detected secondary structure information for 87.2% of amino acids correctly. We also showed preliminarily that 25.2% of amino acid types could be predicted directly from the 3D cryo-EM density map, considering 20 different types in total. Finally, we noted that the prediction runtime of DeepTracer is significantly improved compared to other methods. It predicts a large protein complex structure of more than 30,000 amino acids in only 2 hours. AvailabilityThe repository of this project will be published. Contactdongsi@uw.edu Supplementary informationSupplementary data will be available at Bioinformatics online.

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bioRxiv2020

BioTracs: A transversal framework for computational workflow standardization and traceability

BackgroundThe need of digital tools for integrative analysis is today important in most scientific areas. It leads to several community-driven initiatives to standardize the sharing of data and computational workflows. However, there exists no open agnostic framework to model and implement computation workflows, in particular in bioinformatics. It is therefore difficult for data scientists to share transparently and integrate heterogeneous analysis processes coming from different scientific domains, programing languages, projects or teams. ResultsWe present here BioTracs, a transversal framework for computational workflow standardization and traceability. It is based on PRISM architecture (Process Resource Interfacing SysteM), an agnostic open architecture we introduce here to standardize the way processes and resources can be modelled and interfaced in computational workflows to ensure traceability, reproducibility and facilitate sharing. BioTracs is today implemented in MATLAB and available under open source license on GitHub. Several BioTracs-derived applications are also available online. They were successfully applied to large-scale metabolomics and clinical studies and demonstrated flexibility and robustness. ConclusionsAs an implementation of the PRISM architecture, BioTracs paved the way to an open framework in which bioinformatics could specify ad model workflows. PRISM architecture is designed to provide scalability and transparency from the code to the project level we less efforts. It could also be implemented using open object-oriented languages such as Python, C++ or java. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/951624v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@3775deorg.highwire.dtl.DTLVardef@11b5672org.highwire.dtl.DTLVardef@1c87ff7org.highwire.dtl.DTLVardef@495f2d_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2020

APA-Scan: Detection and Visualization of 3'-UTR APA with RNA-seq and 3'-end-seq Data

BackgroundThe eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3-untranslated region (3-UTR) of mRNA produces transcripts with shorter or longer 3-UTR. Often, 3-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. MethodsAPA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3-UTR annotation and read coverage on the 3-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive users manual are freely available at https://github.com/compbiolabucf/APA-Scan. ResultAPA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3 -UTR APA events and improve genome annotation. ConclusionAPA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3-UTR APA events. The pipeline integrates both RNA-seq and 3-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

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bioRxiv2020

CaptureCompendium: a comprehensive toolkit for 3C analysis.

DNA folding within nuclei is a highly ordered process, with implications for gene regulation and development. An array of chromosome conformation capture (3C) methods have been developed to investigate how DNA is packaged within nuclei and to interrogate specific interactions. While these methods use different approaches to examine target loci (many-versus-all) or the entire genome (all-versus-all), they all rely on the core principle of endonuclease digestion and proximity-based ligation to re-arrange genomic order to reflect the three-dimensional nuclear conformation. This sequence reorganization creates novel chimeric DNA fragments which require specialist bioinformatic tools to analyze and visualize. Despite this need for specialist bioinformatic skills, the core biological importance of genome folding has seen widespread methodological uptake. To service the needs of experimentalists using the many-versus-all Capture-C family of methods we have developed CaptureCompendium; a toolkit of software to simplify the design, analysis and presentation of 3C experiments.

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