PuFFIN--a parameter-free method to build nucleosome maps from paired-end reads.

BackgroundWe 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.ResultsPuFFIN 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.ConclusionsWe 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

The mRNA-bound proteome of the human malaria parasite Plasmodium falciparum.

BackgroundGene expression is controlled at multiple levels, including transcription, stability, translation, and degradation. Over the years, it has become apparent that Plasmodium falciparum exerts limited transcriptional control of gene expression, while at least part of Plasmodium's genome is controlled by post-transcriptional mechanisms. To generate insights into the mechanisms that regulate gene expression at the post-transcriptional level, we undertook complementary computational, comparative genomics, and experimental approaches to identify and characterize mRNA-binding proteins (mRBPs) in P. falciparum.ResultsClose to 1000 RNA-binding proteins are identified by hidden Markov model searches, of which mRBPs encompass a relatively large proportion of the parasite proteome as compared to other eukaryotes. Several abundant mRNA-binding domains are enriched in apicomplexan parasites, while strong depletion of mRNA-binding domains involved in RNA degradation is observed. Next, we experimentally capture 199 proteins that interact with mRNA during the blood stages, 64 of which with high confidence. These captured mRBPs show a significant overlap with the in silico identified candidate RBPs (p < 0.0001). Among the experimentally validated mRBPs are many known translational regulators active in other stages of the parasite's life cycle, such as DOZI, CITH, PfCELF2, Musashi, and PfAlba1-4. Finally, we also detect several proteins with an RNA-binding domain abundant in Apicomplexans (RAP domain) that is almost exclusively found in apicomplexan parasites.ConclusionsCollectively, our results provide the most complete comparative genomics and experimental analysis of mRBPs in P. falciparum. A better understanding of these regulatory proteins will not only give insight into the intricate parasite life cycle but may also provide targets for novel therapeutic strategies

Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features.

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

Nascent RNA sequencing reveals mechanisms of gene regulation in the human malaria parasite Plasmodium falciparum.

Gene expression in Plasmodium falciparum is tightly regulated to ensure successful propagation of the parasite throughout its complex life cycle. The earliest transcriptomics studies in P. falciparum suggested a cascade of transcriptional activity over the course of the 48-hour intraerythrocytic developmental cycle (IDC); however, the just-in-time transcriptional model has recently been challenged by findings that show the importance of post-transcriptional regulation. To further explore the role of transcriptional regulation, we performed the first genome-wide nascent RNA profiling in P. falciparum. Our findings indicate that the majority of genes are transcribed simultaneously during the trophozoite stage of the IDC and that only a small subset of genes is subject to differential transcriptional timing. RNA polymerase II is engaged with promoter regions prior to this transcriptional burst, suggesting that Pol II pausing plays a dominant role in gene regulation. In addition, we found that the overall transcriptional program during gametocyte differentiation is surprisingly similar to the IDC, with the exception of relatively small subsets of genes. Results from this study suggest that further characterization of the molecular players that regulate stage-specific gene expression and Pol II pausing will contribute to our continuous search for novel antimalarial drug targets

Analysis of nucleosome positioning landscapes enables gene discovery in the human malaria parasite Plasmodium falciparum.

BackgroundPlasmodium falciparum, the deadliest malaria-causing parasite, has an extremely AT-rich (80.7 %) genome. Because of high AT-content, sequence-based annotation of genes and functional elements remains challenging. In order to better understand the regulatory network controlling gene expression in the parasite, a more complete genome annotation as well as analysis tools adapted for AT-rich genomes are needed. Recent studies on genome-wide nucleosome positioning in eukaryotes have shown that nucleosome landscapes exhibit regular characteristic patterns at the 5'- and 3'-end of protein and non-protein coding genes. In addition, nucleosome depleted regions can be found near transcription start sites. These unique nucleosome landscape patterns may be exploited for the identification of novel genes. In this paper, we propose a computational approach to discover novel putative genes based exclusively on nucleosome positioning data in the AT-rich genome of P. falciparum.ResultsUsing binary classifiers trained on nucleosome landscapes at the gene boundaries from two independent nucleosome positioning data sets, we were able to detect a total of 231 regions containing putative genes in the genome of Plasmodium falciparum, of which 67 highly confident genes were found in both data sets. Eighty-eight of these 231 newly predicted genes exhibited transcription signal in RNA-Seq data, indicative of active transcription. In addition, 20 out of 21 selected gene candidates were further validated by RT-PCR, and 28 out of the 231 genes showed significant matches using BLASTN against an expressed sequence tag (EST) database. Furthermore, 108 (47%) out of the 231 putative novel genes overlapped with previously identified but unannotated long non-coding RNAs. Collectively, these results provide experimental validation for 163 predicted genes (70.6%). Finally, 73 out of 231 genes were found to be potentially translated based on their signal in polysome-associated RNA-Seq representing transcripts that are actively being translated.ConclusionOur results clearly indicate that nucleosome positioning data contains sufficient information for novel gene discovery. As distinct nucleosome landscapes around genes are found in many other eukaryotic organisms, this methodology could be used to characterize the transcriptome of any organism, especially when coupled with other DNA-based gene finding and experimental methods (e.g., RNA-Seq)

DNA-encoded nucleosome occupancy is associated with transcription levels in the human malaria parasite Plasmodium falciparum.

BackgroundIn eukaryotic organisms, packaging of DNA into nucleosomes controls gene expression by regulating access of the promoter to transcription factors. The human malaria parasite Plasmodium falciparum encodes relatively few transcription factors, while extensive nucleosome remodeling occurs during its replicative cycle in red blood cells. These observations point towards an important role of the nucleosome landscape in regulating gene expression. However, the relation between nucleosome positioning and transcriptional activity has thus far not been explored in detail in the parasite.ResultsHere, we analyzed nucleosome positioning in the asexual and sexual stages of the parasite's erythrocytic cycle using chromatin immunoprecipitation of MNase-digested chromatin, followed by next-generation sequencing. We observed a relatively open chromatin structure at the trophozoite and gametocyte stages, consistent with high levels of transcriptional activity in these stages. Nucleosome occupancy of genes and promoter regions were subsequently compared to steady-state mRNA expression levels. Transcript abundance showed a strong inverse correlation with nucleosome occupancy levels in promoter regions. In addition, AT-repeat sequences were strongly unfavorable for nucleosome binding in P. falciparum, and were overrepresented in promoters of highly expressed genes.ConclusionsThe connection between chromatin structure and gene expression in P. falciparum shares similarities with other eukaryotes. However, the remarkable nucleosome dynamics during the erythrocytic stages and the absence of a large variety of transcription factors may indicate that nucleosome binding and remodeling are critical regulators of transcript levels. Moreover, the strong dependency between chromatin structure and DNA sequence suggests that the P. falciparum genome may have been shaped by nucleosome binding preferences. Nucleosome remodeling mechanisms in this deadly parasite could thus provide potent novel anti-malarial targets

Breastfeeding assessment tools for at-risk and malnourished infants aged under 6 months old: a systematic review

Search strategy produced and used as part of a systematic review to identify breastfeeding assessment tools for use in assessing at-risk and malnourished infants in resource-poor settings. The study performed two separate searches: [1] Ovid interface - Medline database (Version “Ovid MEDLINE (R) In-Process & Other Non-Indexed Citations and Ovid MEDLINE (R) 1946 to Present”) and [2] Ovid interface - Embase database (Version “embase 1980 to 2018 week 21”)

ThIEF: Finding Genome-wide Trajectories of Epigenetics Marks

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