RNA-Seq analysis of splicing in Plasmodium falciparum uncovers new splice junctions, alternative splicing and splicing of antisense transcripts.

Over 50% of genes in Plasmodium falciparum, the deadliest human malaria parasite, contain predicted introns, yet experimental characterization of splicing in this organism remains incomplete. We present here a transcriptome-wide characterization of intraerythrocytic splicing events, as captured by RNA-Seq data from four timepoints of a single highly synchronous culture. Gene model-independent analysis of these data in conjunction with publically available RNA-Seq data with HMMSplicer, an in-house developed splice site detection algorithm, revealed a total of 977 new 5' GU-AG 3' and 5 new 5' GC-AG 3' junctions absent from gene models and ESTs (11% increase to the current annotation). In addition, 310 alternative splicing events were detected in 254 (4.5%) genes, most of which truncate open reading frames. Splicing events antisense to gene models were also detected, revealing complex transcriptional arrangements within the parasite's transcriptome. Interestingly, antisense introns overlap sense introns more than would be expected by chance, perhaps indicating a functional relationship between overlapping transcripts or an inherent organizational property of the transcriptome. Independent experimental validation confirmed over 30 new antisense and alternative junctions. Thus, this largest assemblage of new and alternative splicing events to date in Plasmodium falciparum provides a more precise, dynamic view of the parasite's transcriptome

The lincRNA MIRAT binds to IQGAP1 and modulates the MAPK pathway in NRAS mutant melanoma.

Despite major advances in targeted melanoma therapies, drug resistance limits their efficacy. Long noncoding RNAs (lncRNAs) are transcriptome elements that do not encode proteins but are important regulatory molecules. LncRNAs have been implicated in cancer development and response to different therapeutics and are thus potential treatment targets; however, the majority of their functions and molecular interactions remain unexplored. In this study, we identify a novel cytoplasmic intergenic lincRNA (MIRAT), which is upregulated following prolonged MAPK inhibition in NRAS mutant melanoma and modulates MAPK signaling by binding to the MEK scaffold protein IQGAP1. Collectively, our results present MIRAT's direct modulatory effect on the MAPK pathway and highlight the relevance of cytoplasmic lncRNAs as potential targets in drug resistant cancer

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis

ReCombine: A Suite of Programs for Detection and Analysis of Meiotic Recombination in Whole-Genome Datasets

In meiosis, the exchange of DNA between chromosomes by homologous recombination is a critical step that ensures proper chromosome segregation and increases genetic diversity. Products of recombination include reciprocal exchanges, known as crossovers, and non-reciprocal gene conversions or non-crossovers. The mechanisms underlying meiotic recombination remain elusive, largely because of the difficulty of analyzing large numbers of recombination events by traditional genetic methods. These traditional methods are increasingly being superseded by high-throughput techniques capable of surveying meiotic recombination on a genome-wide basis. Next-generation sequencing or microarray hybridization is used to genotype thousands of polymorphic markers in the progeny of hybrid yeast strains. New computational tools are needed to perform this genotyping and to find and analyze recombination events. We have developed a suite of programs, ReCombine, for using short sequence reads from next-generation sequencing experiments to genotype yeast meiotic progeny. Upon genotyping, the program CrossOver, a component of ReCombine, then detects recombination products and classifies them into categories based on the features found at each location and their distribution among the various chromatids. CrossOver is also capable of analyzing segregation data from microarray experiments or other sources. This package of programs is designed to allow even researchers without computational expertise to use high-throughput, whole-genome methods to study the molecular mechanisms of meiotic recombination

The Long March: A Sample Preparation Technique that Enhances Contig Length and Coverage by High-Throughput Short-Read Sequencing

High-throughput short-read technologies have revolutionized DNA sequencing by drastically reducing the cost per base of sequencing information. Despite producing gigabases of sequence per run, these technologies still present obstacles in resequencing and de novo assembly applications due to biased or insufficient target sequence coverage. We present here a simple sample preparation method termed the “long march” that increases both contig lengths and target sequence coverage using high-throughput short-read technologies. By incorporating a Type IIS restriction enzyme recognition motif into the sequencing primer adapter, successive rounds of restriction enzyme cleavage and adapter ligation produce a set of nested sub-libraries from the initial amplicon library. Sequence reads from these sub-libraries are offset from each other with enough overlap to aid assembly and contig extension. We demonstrate the utility of the long march in resequencing of the Plasmodium falciparum transcriptome, where the number of genomic bases covered was increased by 39%, as well as in metagenomic analysis of a serum sample from a patient with hepatitis B virus (HBV)-related acute liver failure, where the number of HBV bases covered was increased by 42%. We also offer a theoretical optimization of the long march for de novo sequence assembly

HMMSplicer: A Tool for Efficient and Sensitive Discovery of Known and Novel Splice Junctions in RNA-Seq Data

Background: High-throughput sequencing of an organism’s transcriptome, or RNA-Seq, is a valuable and versatile new strategy for capturing snapshots of gene expression. However, transcriptome sequencing creates a new class of alignment problem: mapping short reads that span exon-exon junctions back to the reference genome, especially in the case where a splice junction is previously unknown. Methodology/Principal Findings: Here we introduce HMMSplicer, an accurate and efficient algorithm for discovering canonical and non-canonical splice junctions in short read datasets. HMMSplicer identifies more splice junctions than currently available algorithms when tested on publicly available A. thaliana, P. falciparum, and H. sapiens datasets without a reduction in specificity. Conclusions/Significance: HMMSplicer was found to perform especially well in compact genomes and on genes with low expression levels, alternative splice isoforms, or non-canonical splice junctions. Because HHMSplicer does not rely on prebuilt gene models, the products of inexact splicing are also detected. For H. sapiens, we find 3.6 % of 39 splice sites and 1.4% of 59 splice sites are inexact, typically differing by 3 bases in either direction. In addition, HMMSplicer provides a score for every predicted junction allowing the user to set a threshold to tune false positive rates depending on the needs of the experiment. HMMSplicer is implemented in Python. Code and documentation are freely available a

RNA-seq analyses of blood-induced changes in gene expression in the mosquito vector species, Aedes aegypti

<p>Abstract</p> <p>Background</p> <p>Hematophagy is a common trait of insect vectors of disease. Extensive genome-wide transcriptional changes occur in mosquitoes after blood meals, and these are related to digestive and reproductive processes, among others. Studies of these changes are expected to reveal molecular targets for novel vector control and pathogen transmission-blocking strategies. The mosquito <it>Aedes aegypti </it>(Diptera, Culicidae), a vector of Dengue viruses, Yellow Fever Virus (YFV) and Chikungunya virus (CV), is the subject of this study to look at genome-wide changes in gene expression following a blood meal.</p> <p>Results</p> <p>Transcriptional changes that follow a blood meal in <it>Ae. aegypti </it>females were explored using RNA-seq technology. Over 30% of more than 18,000 investigated transcripts accumulate differentially in mosquitoes at five hours after a blood meal when compared to those fed only on sugar. Forty transcripts accumulate only in blood-fed mosquitoes. The list of regulated transcripts correlates with an enhancement of digestive activity and a suppression of environmental stimuli perception and innate immunity. The alignment of more than 65 million high-quality short reads to the <it>Ae. aegypti </it>reference genome permitted the refinement of the current annotation of transcript boundaries, as well as the discovery of novel transcripts, exons and splicing variants. <it>Cis</it>-regulatory elements (CRE) and <it>cis</it>-regulatory modules (CRM) enriched significantly at the 5'end flanking sequences of blood meal-regulated genes were identified.</p> <p>Conclusions</p> <p>This study provides the first global view of the changes in transcript accumulation elicited by a blood meal in <it>Ae. aegypti </it>females. This information permitted the identification of classes of potentially co-regulated genes and a description of biochemical and physiological events that occur immediately after blood feeding. The data presented here serve as a basis for novel vector control and pathogen transmission-blocking strategies including those in which the vectors are modified genetically to express anti-pathogen effector molecules.</p

IMSA: Integrated metagenomic sequence analysis for identification of exogenous reads in a host genomic background

Metagenomics, the study of microbial genomes within diverse environments, is a rapidly developing field. The identification of microbial sequences within a host organism enables the study of human intestinal, respiratory, and skin microbiota, and has allowed the identification of novel viruses in diseases such as Merkel cell carcinoma. There are few publicly available tools for metagenomic high throughput sequence analysis. We present Integrated Metagenomic Sequence Analysis (IMSA), a flexible, fast, and robust computational analysis pipeline that is available for public use. IMSA takes input sequence from high throughput datasets and uses a user-defined host database to filter out host sequence. IMSA then aligns the filtered reads to a user-defined universal database to characterize exogenous reads within the host background. IMSA assigns a score to each node of the taxonomy based on read frequency, and can output this as a taxonomy report suitable for cluster analysis or as a taxonomy map (TaxMap). IMSA also outputs the specific sequence reads assigned to a taxon of interest for downstream analysis. We demonstrate the use of IMSA to detect pathogens and normal flora within sequence data from a primary human cervical cancer carrying HPV16, a primary human cutaneous squamous cell carcinoma carrying HPV 16, the CaSki cell line carrying HPV16, and the HeLa cell line carrying HPV18

Genomic approaches to the study of splicing in Plasmodium falciparum and other organisms using high throughput sequencing

In the last five years, high throughput sequencing has revolutionized biological research. The ability to quickly generate millions of short sequence reads enables studies that would have been inconceivable even 10 years ago. This work focuses on RNA-Seq, the application of high throughput sequencing to an organism's transcriptome. We describe a method of library preparation that improves sequence coverage, a new algorithm for detecting splice junctions in the datasets, and finally, application of these techniques to the study of splicing in Plasmodium falciparum.The long march is a technique for Solexa library preparation that increases contig length and target sequence coverage. The long march incorporates a Type IIS restriction enzyme into the sequencing primer adapter. Each round of marching cuts off the initial part of the read and ligates a new adapter downstream, creating overlapping reads. Validation on P. falciparum genomic and human hepatitis B virus positive samples showed 39% and 42%, respectively, increases in numbers of bases covered.Next we developed an algorithm to detect spliced reads crossing exon-exon junctions in RNA-Seq datasets. Our algorithm uses an unbiased approach, relying only on the read dataset and a reference genome, detecting canonical and noncanonical splice junctions. This works by dividing reads in half for initial seeding in the reference genome then using an HMM, trained on the input data, to determine the optimal splice position. Our algorithm provides a score for each splice junction, which allows researchers to tune the false positive rate to the requirements of their experiment. This approach identifies more splice junctions than currently available algorithms, without a reduction in specificity, when tested on publicly available datasets for Arabidopsis thaliana, Plasmodium falciparum, and Homo sapiens.Finally, our library preparation technique and splice detection algorithm were used to study splicing in P. falciparum. Both our data and publicly available datasets were used to identify splicing events in the blood stages of the parasite. We confirmed 6,678 previously known introns and identified 977 novel introns with canonical splice edges. In addition, we detected 310 alternative slicing events as well as splicing events antisense to known transcripts