The Genomedata format for storing large-scale functional genomics data

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

BACKGROUND

Transcription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult.

RESULTS

Here, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs.

CONCLUSIONS

Using known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

Continuous chromatin state feature annotation of the human epigenome

Motivation

Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity, such as ChIP-seq measurements of histone modification and transcription factor binding. They output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm-nonneg, that uses a non-negative state space model to efficiently annotate the genome with chromatin state features. We also propose several measures of the quality of a chromatin state feature annotation and we compare the performance of several alternative methods according to these quality measures.

Results

We show that chromatin state features from epigenome-ssm-nonneg are more useful for several downstream applications than both continuous and discrete alternatives, including their ability to identify expressed genes and enhancers. Therefore, we expect that these continuous chromatin state features will be valuable reference annotations to be used in visualization and downstream analysis.

Availability and implementation

Source code for epigenome-ssm is available at https://github.com/habibdanesh/epigenome-ssm and Zenodo (DOI: 10.5281/zenodo.6507585).

Supplementary information

Supplementary data are available at Bioinformatics online.

BD5: An open HDF5-based data format to represent quantitative biological dynamics data

BD5 is a new binary data format based on HDF5 (hierarchical data format version 5). It can be used for representing quantitative biological dynamics data obtained from bioimage informatics techniques and mechanobiological simulations. Biological Dynamics Markup Language (BDML) is an XML (Extensible Markup Language)-based open format that is also used to represent such data; however, it becomes difficult to access quantitative data in BDML files when the file size is large because parsing XML-based files requires large computational resources to first read the whole file sequentially into computer memory. BD5 enables fast random (i.e., direct) access to quantitative data on disk without parsing the entire file. Therefore, it allows practical reuse of data for understanding biological mechanisms underlying the dynamics.

Benchmarking database systems for Genomic Selection implementation

MOTIVATION

With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems.

RESULTS

We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix.

AVAILABILITY

http://gobiin1.bti.cornell.edu:6083/projects/GBM/repos/benchmarking/browse.

Set2 methyltransferase facilitates cell cycle progression by maintaining transcriptional fidelity

Methylation of histone H3 lysine 36 (H3K36me) by yeast Set2 is critical for the maintenance of chromatin structure and transcriptional fidelity. However, we do not know the full range of Set2/H3K36me functions or the scope of mechanisms that regulate Set2-dependent H3K36 methylation. Here, we show that the APC/C^CDC20 complex regulates Set2 protein abundance during the cell cycle. Significantly, absence of Set2-mediated H3K36me causes a loss of cell cycle control and pronounced defects in the transcriptional fidelity of cell cycle regulatory genes, a class of genes that are generally long, hence highly dependent on Set2/H3K36me for their transcriptional fidelity. Because APC/C also controls human SETD2, and SETD2 likewise regulates cell cycle progression, our data imply an evolutionarily conserved cell cycle function for Set2/SETD2 that may explain why recurrent mutations of SETD2 contribute to human disease.

Genome contact map explorer: a platform for the comparison, interactive visualization and analysis of genome contact maps

Hi-C experiments generate data in form of large genome contact maps (Hi-C maps). These show that chromosomes are arranged in a hierarchy of three-dimensional compartments. But to understand how these compartments form and by how much they affect genetic processes such as gene regulation, biologists and bioinformaticians need efficient tools to visualize and analyze Hi-C data. However, this is technically challenging because these maps are big. In this paper, we remedied this problem, partly by implementing an efficient file format and developed the genome contact map explorer platform. Apart from tools to process Hi-C data, such as normalization methods and a programmable interface, we made a graphical interface that let users browse, scroll and zoom Hi-C maps to visually search for patterns in the Hi-C data. In the software, it is also possible to browse several maps simultaneously and plot related genomic data. The software is openly accessible to the scientific community.

eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines

BACKGROUND

Enhancers are tissue specific distal regulation elements, playing vital roles in gene regulation and expression. The prediction and identification of enhancers are important but challenging issues for bioinformatics studies. Existing computational methods, mostly single classifiers, can only predict the transcriptional coactivator EP300 based enhancers and show low generalization performance.

RESULTS

We built a hybrid classifier called eRFSVM in this study, using random forests as a base classifier, and support vector machines as a main classifier. eRFSVM integrated two components as eRFSVM-ENCODE and eRFSVM-FANTOM5 with diverse features and labels. The base classifier trained datasets from a single tissue or cell with random forests. The main classifier made the final decision by support vector machines algorithm, with the predicting results of base classifiers as inputs. For eRFSVM-ENCODE, we trained datasets from cell lines including Gm12878, Hep, H1-hesc and Huvec, using ChIP-Seq datasets as features and EP300 based enhancers as labels. We tested eRFSVM-ENCODE on K562 dataset, and resulted in a predicting precision of 83.69 %, which was much better than existing classifiers. For eRFSVM-FANTOM5, with enhancers identified by RNA in FANTOM5 project as labels, the precision, recall, F-score and accuracy were 86.17 %, 36.06 %, 50.84 % and 93.38 % using eRFSVM, increasing 23.24 % (69.92 %), 97.05 % (18.30 %), 76.90 % (28.74 %), 4.69 % (89.20 %) than the existing algorithm, respectively.

CONCLUSIONS

All these results demonstrated that eRFSVM was a better classifier in predicting both EP300 based and FAMTOM5 RNAs based enhancers.

CWig: compressed representation of Wiggle/BedGraph format

MOTIVATION

BigWig, a format to represent read density data, is one of the most popular data types. They can represent the peak intensity in ChIP-seq, the transcript expression in RNA-seq, the copy number variation in whole genome sequencing, etc. UCSC Encode project uses the bigWig format heavily for storage and visualization. Of 5.2 TB Encode hg19 database, 1.6 TB (31% of the total space) is used to store bigWig files. BigWig format not only saves a lot of space but also supports fast queries that are crucial for interactive analysis and browsing. In our benchmark, bigWig often has similar size to the gzipped raw data, while is still able to support ∼ 5000 random queries per second.

RESULTS

Although bigWig is good enough at the moment, both storage space and query time are expected to become limited when sequencing gets cheaper. This article describes a new method to store density data named CWig. The format uses on average one-third of the size of existing bigWig files and improves random query speed up to 100 times.

AVAILABILITY AND IMPLEMENTATION

http://genome.ddns.comp.nus.edu.sg/∼cwig.

metaseq: a Python package for integrative genome-wide analysis reveals relationships between chromatin insulators and associated nuclear mRNA

Here we introduce metaseq, a software library written in Python, which enables loading multiple genomic data formats into standard Python data structures and allows flexible, customized manipulation and visualization of data from high-throughput sequencing studies. We demonstrate its practical use by analyzing multiple datasets related to chromatin insulators, which are DNA-protein complexes proposed to organize the genome into distinct transcriptional domains. Recent studies in Drosophila and mammals have implicated RNA in the regulation of chromatin insulator activities. Moreover, the Drosophila RNA-binding protein Shep has been shown to antagonize gypsy insulator activity in a tissue-specific manner, but the precise role of RNA in this process remains unclear. Better understanding of chromatin insulator regulation requires integration of multiple datasets, including those from chromatin-binding, RNA-binding, and gene expression experiments. We use metaseq to integrate RIP- and ChIP-seq data for Shep and the core gypsy insulator protein Su(Hw) in two different cell types, along with publicly available ChIP-chip and RNA-seq data. Based on the metaseq-enabled analysis presented here, we propose a model where Shep associates with chromatin cotranscriptionally, then is recruited to insulator complexes in trans where it plays a negative role in insulator activity.

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.

Identifying elemental genomic track types and representing them uniformly

BACKGROUND

With the recent advances and availability of various high-throughput sequencing technologies, data on many molecular aspects, such as gene regulation, chromatin dynamics, and the three-dimensional organization of DNA, are rapidly being generated in an increasing number of laboratories. The variation in biological context, and the increasingly dispersed mode of data generation, imply a need for precise, interoperable and flexible representations of genomic features through formats that are easy to parse. A host of alternative formats are currently available and in use, complicating analysis and tool development. The issue of whether and how the multitude of formats reflects varying underlying characteristics of data has to our knowledge not previously been systematically treated.

RESULTS

We here identify intrinsic distinctions between genomic features, and argue that the distinctions imply that a certain variation in the representation of features as genomic tracks is warranted. Four core informational properties of tracks are discussed: gaps, lengths, values and interconnections. From this we delineate fifteen generic track types. Based on the track type distinctions, we characterize major existing representational formats and find that the track types are not adequately supported by any single format. We also find, in contrast to the XML formats, that none of the existing tabular formats are conveniently extendable to support all track types. We thus propose two unified formats for track data, an improved XML format, BioXSD 1.1, and a new tabular format, GTrack 1.0.

CONCLUSIONS

The defined track types are shown to capture relevant distinctions between genomic annotation tracks, resulting in varying representational needs and analysis possibilities. The proposed formats, GTrack 1.0 and BioXSD 1.1, cater to the identified track distinctions and emphasize preciseness, flexibility and parsing convenience.

A new efficient data structure for storage and retrieval of multiple biosequences

Today's genome analysis applications require sequence representations allowing for fast access to their contents while also being memory-efficient enough to facilitate analyses of large-scale data. While a wide variety of sequence representations exist, lack of a generic implementation of efficient sequence storage has led to a plethora of poorly reusable or programming language-specific implementations. We present a novel, space-efficient data structure (GtEncseq) for storing multiple biological sequences of variable alphabet size, with customizable character transformations, wildcard support and an assortment of internal representations optimized for different distributions of wildcards and sequence lengths. For the human genome (3.1 gigabases, including 237 million wildcard characters) our representation requires only 2 + 8 × 10^-6bits per character. Implemented in C, our portable software implementation provides a variety of methods for random and sequential access to characters and substrings (including different reading directions) using an object-oriented interface. In addition, it includes access to metadata like sequence descriptions or character distributions. The library is extensible to be used from various scripting languages. GtEncseq is much more versatile than previous solutions, adding features that were previously unavailable. Benchmarks show that it is competitive with respect to space and time requirements.

Exploratory analysis of genomic segmentations with Segtools

Background

As genome-wide experiments and annotations become more prevalent, researchers increasingly require tools to help interpret data at this scale. Many functional genomics experiments involve partitioning the genome into labeled segments, such that segments sharing the same label exhibit one or more biochemical or functional traits. For example, a collection of ChlP-seq experiments yields a compendium of peaks, each labeled with one or more associated DNA-binding proteins. Similarly, manually or automatically generated annotations of functional genomic elements, including cis-regulatory modules and protein-coding or RNA genes, can also be summarized as genomic segmentations.

Results

We present a software toolkit called Segtools that simplifies and automates the exploration of genomic segmentations. The software operates as a series of interacting tools, each of which provides one mode of summarization. These various tools can be pipelined and summarized in a single HTML page. We describe the Segtools toolkit and demonstrate its use in interpreting a collection of human histone modification data sets and Plasmodium falciparum local chromatin structure data sets.

Conclusions

Segtools provides a convenient, powerful means of interpreting a genomic segmentation.