Empirical evidence suggests that the malaria parasite Plasmodium falciparum employs a broad range of mechanisms to regulate gene transcription throughout the organism's complex life cycle. To better understand this regulatory machinery, we assembled a rich collection of genomic and epigenomic data sets, including information about transcription factor (TF) binding motifs, patterns of covalent histone modifications, nucleosome occupancy, GC content, and global 3D genome architecture. We used these data to train machine learning models to discriminate between high-expression and low-expression genes, focusing on three distinct stages of the red blood cell phase of the Plasmodium life cycle. Our results highlight the importance of histone modifications and 3D chromatin architecture in Plasmodium transcriptional regulation and suggest that AP2 transcription factors may play a limited regulatory role, perhaps operating in conjunction with epigenetic factors.

We address the problem of comparing multiple genome-wide maps representing nucleosome positions or specific histone marks. These maps can originate from the comparative analysis of ChIP-Seq/MNase-Seq/FAIRE-Seq data for different cell types/tissues or multiple time points. The input to the problem is a set of maps, each of which is a list of genomics locations for nucleosomes or histone marks. The output is an alignment of nucleosomes/histone marks across time points (that we call trajectories), allowing small movements and gaps in some of the maps. We present a tool called ThIEF (TrackIng of Epigenetic Features) that can efficiently compute these trajectories. ThIEF comes into two "flavors": ThIEF:Iterative finds the trajectories progressively using bipartite matching, while ThIEF:LP solves a k-partite matching problem on a hyper graph using linear programming. ThIEF:LP is guaranteed to find the optimal solution, but it is slower than ThIEF:Iterative. We demonstrate the utility of ThIEF by providing an example of applications on the analysis of temporal nucleosome maps for the human malaria parasite. As a surprisingly remarkable result, we show that the output of ThIEF can be used to produce a supervised classifier that can accurately predict the position of stable nucleosomes (i.e., nucleosomes present in all time points) and unstable nucleosomes (i.e., present in at most half of the time points) from the primary DNA sequence. To the best of our knowledge, this is the first result on the prediction of the dynamics of nucleosomes solely based on their DNA binding preference. Software is available at https://github.com/ucrbioinfo/ThIEF.

BACKGROUND: We introduce a novel method, called PuFFIN, that takes advantage of paired-end short reads to build genome-wide nucleosome maps with larger numbers of detected nucleosomes and higher accuracy than existing tools. In contrast to other approaches that require users to optimize several parameters according to their data (e.g., the maximum allowed nucleosome overlap or legal ranges for the fragment sizes) our algorithm can accurately determine a genome-wide set of non-overlapping nucleosomes without any user-defined parameter. This feature makes PuFFIN significantly easier to use and prevents users from choosing the "wrong" parameters and obtain sub-optimal nucleosome maps. RESULTS: PuFFIN builds genome-wide nucleosome maps using a multi-scale (or multi-resolution) approach. Our algorithm relies on a set of nucleosome "landscape" functions at different resolution levels: each function represents the likelihood of each genomic location to be occupied by a nucleosome for a particular value of the smoothing parameter. After a set of candidate nucleosomes is computed for each function, PuFFIN produces a consensus set that satisfies non-overlapping constraints and maximizes the number of nucleosomes. CONCLUSIONS: We report comprehensive experimental results that compares PuFFIN with recently published tools (NOrMAL, TEMPLATE FILTERING, and NucPosSimulator) on several synthetic datasets as well as real data for S. cerevisiae and P. falciparum. Experimental results show that our approach produces more accurate nucleosome maps with a higher number of non-overlapping nucleosomes than other tools.

SUMMARY: We introduce BRAT-BW, a fast, accurate and memory-efficient tool that maps bisulfite-treated short reads (BS-seq) to a reference genome using the FM-index (Burrows-Wheeler transform). BRAT-BW is significantly more memory efficient and faster on longer reads than current state-of-the-art tools for BS-seq data, without compromising on accuracy. BRAT-BW is a part of a software suite for genome-wide single base-resolution methylation data analysis that supports single and paired-end reads and includes a tool for estimation of methylation level at each cytosine. AVAILABILITY: The software is available in the public domain at http://compbio.cs.ucr.edu/brat/.

MOTIVATION: Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome positions for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome; then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle more complex configurations. Also, they can only provide the positions of nucleosomes and their occupancy level, whereas it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes and/or whether nucleosomes are well positioned or 'fuzzy' in the sequenced cell sample. RESULTS: We address these issues by providing a novel method based on a parametric probabilistic model. An expectation maximization algorithm is used to infer the parameters of the mixture of distributions. We compare the performance of our method on two real datasets against Template Filtering, which is considered the current state-of-the-art. On synthetic data, we show that our method can resolve more accurately complex configurations of nucleosomes, and it is more robust to user-defined parameters. On real data, we show that our method detects a significantly higher number of nucleosomes. AVAILABILITY: Visit http://www.cs.ucr.edu/~polishka.

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.

Abstract Summary: We present a new, accurate and efficient tool for mapping short reads obtained from the Illumina Genome Analyzer following sodium bisulfite conversion. Our tool, BRAT, supports single and paired-end reads and handles input files containing reads and mates of different lengths. BRAT is faster, maps more unique paired-end reads and has higher accuracy than existing programs. The software package includes tools to end-trim low-quality bases of the reads and to report nucleotide counts for mapped reads on the reference genome. Availability: The source code is freely available for download at http://compbio.cs.ucr.edu/brat/ and is distributed as Open Source software under the GPLv3.0. Contact: [email protected]

Bioinformatics (2010) 26(4), 572–573 The authors would like to apologize for the erroneous statements on the accuracy of the mapping for mrsFAST (Hormozdiari et al., 2009). The parameters for the insert size used for msrFAST to obtain Figure 1 and Table 1 were not correct; using the correct parameters would have yield higher accuracy than the one reported. At the time of the initial simulation there was no documentation available regarding the distance between the reads for paired-end, so we deduced the definition for the distance from the output files of mrsFAST. For 32 bases paired-end reads (Table 1), we used insert size range from 106 to 306 (called min and max, respectively, in mrsFAST), but the correct values should have been [43, 243]. For 24 bases paired-end reads (Fig. 1), we used [136, 324] whereas the correct values should have been [89, 277]. For 64 bases paired-end reads (Fig. 1), we used [136, 364], but the correct parameters should have been [9, 237].

Malaria is the cause of significant global morbidity and mortality with 300-500 million cases annually. Despite its disease burden relatively little is known about the molecular biology of the pathogen that causes malaria. For example, the completion of the genome sequence of Plasmodium falciparum, the species responsible for the most severe form of human malaria revealed that only 35% of the genes code for proteins with an identifiable function. In addition, little is known about how transcription and translation are regulated. The absence of routine genetic tools for studying Plasmodium parasites suggests that these numbers are unlikely to change quickly if conventional, serial, biological methods are used to study the parasite. We are using high-density oligonucleotide arrays and informatic methods to study the genome of the malaria parasite with the goals of understanding how expression is regulated, functionally cataloging the genome, discovering allelic variation and identifying new therapeutic targets. We have shown that genes with highly correlated levels and temporal patterns of expression are often involved in similar functions or cellular processes suggesting that expression profiling can be used to rapidly predict function. In addition we find that there is good correlation between protein levels and transcript levels, suggesting that regulation of expression occurs transcriptionally. Analysis of whole-genome transcription patterns reveals that the chromosome is organized into regions that are transcriptionally active and transcriptionally silent in the intraerythrocytic stage of the parasite's lifecycle. Thus, both the timing and the relative level of transcription in the parasite is organized into position-dependent domains suggesting that transcription may be regulated at least partially at the level of chromatin structure.