Genome sequencing and application of Taiwanese macaque Macaca cyclopis.

Formosan macaque (Macaca cyclopis) is the only non-human primate in Taiwan Island. We performed de novo hybrid assembly for M. cyclopis using Illumina paired-end short reads, mate-pair reads and Nanopore long reads and obtained 5065 contigs with a N50 of 2.66 megabases. M. cyclopis contigs > = 10 kb were assigned to chromosomes using Indian rhesus macaque (Macaca mulatta mulatta) genome assembly Mmul_10 as reference, resulting in a draft of M. cyclopis genome of 2,846,042,475 bases, distributed in 21 chromosomes. The draft genome contains 23,462 transcriptional origins (genes), capable of expressing 716,231 exons in 59,484 transcripts. Genome-based phylogenetic study using the assembled M. cyclopis genome together with genomes of four other macaque species, human, orangutan and chimpanzee showed similar result as previously reported. However, the M. cyclopis species was found to diverge from Chinese M. mulatta lasiota about 1.8 million years ago. Fossil gene analysis detected the presence of gap and pol endogenous viral elements of simian retrovirus in all macaques tested, including M. fascicularis, M. m. mulatta and M. cyclopis. However, M. cyclopis showed ~ 2 times less in number and more uniform in chromosomal locations. The constrain in foreign genome disturbance, presumably due to geographical isolation, should be able to simplify genomics-related investigations, making M. cyclopis an ideal primate species for medical research.

Transcript Profiling Analysis Through Paired-End Ditag (PET) Approach Coupled with Deep Sequencing Reveals Transcriptome Complexity in Yeast.

The identification of structural and functional elements encoded in a genome is a challenging task. Although the transcriptome of budding yeast has been extensively analyzed, the boundaries and untranslated regions of yeast genes remain elusive. To address this least-explored field of yeast genomics, we performed a transcript profiling analysis through paired-end ditag (PET) approach coupled with deep sequencing. With 562,133 PET sequences we accurately defined the boundaries and untranslated regions of 3,409 ORFs, suggesting many yeast genes have multiple transcription start sites (TSSs). We also identified 85 previously uncharacterized transcripts either in intergenic regions or from the opposite strand of reported genomic features. Furthermore, our data revealed the extensive 3' end heterogeneity of yeast genes and identified a novel putative motif for polyadenylation. This study would serve as an invaluable resource for elucidating the regulation and evolution of yeast genes. Here we present a detailed protocol with minor modifications, which could be broadly applied to investigate transcripts from budding yeast to mammalian organisms.

Genomic analyses of unique carbohydrate and phytohormone metabolism in the macroalga Gracilariopsis lemaneiformis (Rhodophyta).

BACKGROUND: Red algae are economically valuable for food and in industry. However, their genomic information is limited, and the genomic data of only a few species of red algae have been sequenced and deposited recently. In this study, we annotated a draft genome of the macroalga Gracilariopsis lemaneiformis (Gracilariales, Rhodophyta). RESULTS: The entire 88.98 Mb genome of Gp. lemaneiformis 981 was generated from 13,825 scaffolds (≥500 bp) with an N50 length of 30,590 bp, accounting for approximately 91% of this algal genome. A total of 38.73 Mb of scaffold sequences were repetitive, and 9281 protein-coding genes were predicted. A phylogenomic analysis of 20 genomes revealed the relationship among the Chromalveolata, Rhodophyta, Chlorophyta and higher plants. Homology analysis indicated phylogenetic proximity between Gp. lemaneiformis and Chondrus crispus. The number of enzymes related to the metabolism of carbohydrates, including agar, glycoside hydrolases, glycosyltransferases, was abundant. In addition, signaling pathways associated with phytohormones such as auxin, salicylic acid and jasmonates are reported for the first time for this alga. CONCLUSION: We sequenced and analyzed a draft genome of the red alga Gp. lemaneiformis, and revealed its carbohydrate metabolism and phytohormone signaling characteristics. This work will be helpful in research on the functional and comparative genomics of the order Gracilariales and will enrich the genomic information on marine algae.

Identification of the BRD1 interaction network and its impact on mental disorder risk.

BACKGROUND: The bromodomain containing 1 (BRD1) gene has been implicated with transcriptional regulation, brain development, and susceptibility to schizophrenia and bipolar disorder. To advance the understanding of BRD1 and its role in mental disorders, we characterized the protein and chromatin interactions of the BRD1 isoforms, BRD1-S and BRD1-L. METHODS: Stable human cell lines expressing epitope tagged BRD1-S and BRD1-L were generated and used as discovery systems for identifying protein and chromatin interactions. Protein-protein interactions were identified using co-immunoprecipitation followed by mass spectrometry and chromatin interactions were identified using chromatin immunoprecipitation followed by next generation sequencing. Gene expression profiles and differentially expressed genes were identified after upregulating and downregulating BRD1 expression using microarrays. The presented functional molecular data were integrated with human genomic and transcriptomic data using available GWAS, exome-sequencing datasets as well as spatiotemporal transcriptomic datasets from the human brain. RESULTS: We present several novel protein interactions of BRD1, including isoform-specific interactions as well as proteins previously implicated with mental disorders. By BRD1-S and BRD1-L chromatin immunoprecipitation followed by next generation sequencing we identified binding to promoter regions of 1540 and 823 genes, respectively, and showed correlation between BRD1-S and BRD1-L binding and regulation of gene expression. The identified BRD1 interaction network was found to be predominantly co-expressed with BRD1 mRNA in the human brain and enriched for pathways involved in gene expression and brain function. By interrogation of large datasets from genome-wide association studies, we further demonstrate that the BRD1 interaction network is enriched for schizophrenia risk. CONCLUSION: Our results show that BRD1 interacts with chromatin remodeling proteins, e.g. PBRM1, as well as histone modifiers, e.g. MYST2 and SUV420H1. We find that BRD1 primarily binds in close proximity to transcription start sites and regulates expression of numerous genes, many of which are involved with brain development and susceptibility to mental disorders. Our findings indicate that BRD1 acts as a regulatory hub in a comprehensive schizophrenia risk network which plays a role in many brain regions throughout life, implicating e.g. striatum, hippocampus, and amygdala at mid-fetal stages.

MALAT1 long ncRNA promotes gastric cancer metastasis by suppressing PCDH10.

EZH2, the catalytic component of polycomb repressive complex 2 (PRC2), is frequently overexpressed in human cancers and contributes to tumor initiation and progression, in part through transcriptional silencing of tumor suppressor genes. A number of noncoding RNAs (ncRNAs) recruit EZH2 to specific chromatin loci, where they modulate gene expression. Here, we used RNA immunoprecipitation sequencing (RIP-seq) to profile EZH2-associated transcripts in human gastric cancer cell lines. We identified 8,256 transcripts, including both noncoding and coding transcripts, some of which were derived from cancer-related loci. In particular, we found that long noncoding RNA (lncRNA) MALAT1 binds EZH2, suppresses the tumor suppressor PCDH10, and promotes gastric cellular migration and invasion. Our work thus provides a global view of the EZH2-associated transcriptome and offers new insight into the function of EZH2 in gastric tumorigenesis.

Genome-wide profiling of polyadenylation sites reveals a link between selective polyadenylation and cancer metastasis.

Alternative polyadenylation (APA) is an important post-transcriptional modification implicated in many diseases, including cancer. Although extensively characterized, the functional consequence of APA modulation on tumorigenesis remains elusive. Here, we developed a deep sequencing-based approach that specifically profiles 3' termini of polyadenylated RNAs (herein termed 3T-seq) and analyzed APA events in two gastric cancer cell lines and one non-transformed counterpart. Overall, we identified >28 000 poly(A) sites, 70% of which are potentially novel. Further, we observed widespread APA-mediated 3' UTR shortening of 513 genes (false discovery rate < 0.05) across gastric cancer genome. We characterized one of these genes, NET1, in detail and found that the shortening of NET1 3' UTR significantly enhances transcriptional activity. Moreover, the NET1 isoform with short 3' UTR promotes cellular migration and invasion in vitro. Collectively, our work provides an effective approach for genome-wide APA site profiling and reveals a link between APA modulation and gastric cancer metastasis.

Genome-wide profiling of untranslated regions by paired-end ditag sequencing reveals unexpected transcriptome complexity in yeast.

The identification of structural and functional elements encoded in a genome is a challenging task. Although the transcriptome of budding yeast has been extensively analyzed, the boundaries and untranslated regions of yeast genes remain elusive. To address this least-explored field of yeast genomics, we performed a transcript profiling analysis through paired-end ditag (PET) approach coupled with deep sequencing. With 562,133 PET sequences we accurately defined the boundaries and untranslated regions of 3,409 ORFs, suggesting many yeast genes have multiple transcription start sites (TSSs). We also identified 85 previously uncharacterized transcripts either in intergenic regions or from the opposite strand of reported genomic features. Furthermore, our data revealed the extensive 3' end heterogeneity of yeast genes and identified a novel putative motif for polyadenylation. Our results indicate the yeast transcriptome is more complex than expected. This study would serve as an invaluable resource for elucidating the regulation and evolution of yeast genes.

Biomolecular pathway databases.

From the database point of view, biomolecular pathways are sets of proteins and other biomacromolecules that represent spatio-temporally organized cascades of interactions with the involvement of low-molecular compounds and are responsible for achieving specific phenotypic biological outcomes. A pathway is usually associated with certain subcellular compartments. In this chapter, we analyze the major public biomolecular pathway databases. Special attention is paid to database scope, completeness, issues of annotation reliability, and pathway classification. In addition, systems for information retrieval, tools for mapping user-defined gene sets onto the information in pathway databases, and their typical research applications are reviewed. Whereas today, pathway databases contain almost exclusively qualitative information, the desired trend is toward quantitative description of interactions and reactions in pathways, which will gradually enable predictive modeling and transform the pathway databases into analytical workbenches.

Databases of protein-protein interactions and complexes.

In the current understanding, translation of genomic sequences into proteins is the most important path for realization of genome information. In exercising their intended function, proteins work together through various forms of direct (physical) or indirect interaction mechanisms. For a variety of basic functions, many proteins form a large complex representing a molecular machine or a macromolecular super-structural building block. After several high-throughput techniques for detection of protein-protein interactions had matured, protein interaction data became available in a large scale and curated databases for protein-protein interactions (PPIs) are a new necessity for efficient research. Here, their scope, annotation quality, and retrieval tools are reviewed. In addition, attention is paid to portals that provide unified access to a variety of such databases with added annotation value.

ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing.

Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) is a new technology to study genome-wide long-range chromatin interactions bound by protein factors. Here we present ChIA-PET Tool, a software package for automatic processing of ChIA-PET sequence data, including linker filtering, mapping tags to reference genomes, identifying protein binding sites and chromatin interactions, and displaying the results on a graphical genome browser. ChIA-PET Tool is fast, accurate, comprehensive, user-friendly, and open source (available at http://chiapet.gis.a-star.edu.sg).

Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset.

BACKGROUND: Algorithms designed to predict protein disorder play an important role in structural and functional genomics, as disordered regions have been reported to participate in important cellular processes. Consequently, several methods with different underlying principles for disorder prediction have been independently developed by various groups. For assessing their usability in automated workflows, we are interested in identifying parameter settings and threshold selections, under which the performance of these predictors becomes directly comparable. RESULTS: First, we derived a new benchmark set that accounts for different flavours of disorder complemented with a similar amount of order annotation derived for the same protein set. We show that, using the recommended default parameters, the programs tested are producing a wide range of predictions at different levels of specificity and sensitivity. We identify settings, in which the different predictors have the same false positive rate. We assess conditions when sets of predictors can be run together to derive consensus or complementary predictions. This is useful in the framework of proteome-wide applications where high specificity is required such as in our in-house sequence analysis pipeline and the ANNIE webserver. CONCLUSIONS: This work identifies parameter settings and thresholds for a selection of disorder predictors to produce comparable results at a desired level of specificity over a newly derived benchmark dataset that accounts equally for ordered and disordered regions of different lengths.

An oestrogen-receptor-alpha-bound human chromatin interactome.

Genomes are organized into high-level three-dimensional structures, and DNA elements separated by long genomic distances can in principle interact functionally. Many transcription factors bind to regulatory DNA elements distant from gene promoters. Although distal binding sites have been shown to regulate transcription by long-range chromatin interactions at a few loci, chromatin interactions and their impact on transcription regulation have not been investigated in a genome-wide manner. Here we describe the development of a new strategy, chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) for the de novo detection of global chromatin interactions, with which we have comprehensively mapped the chromatin interaction network bound by oestrogen receptor alpha (ER-alpha) in the human genome. We found that most high-confidence remote ER-alpha-binding sites are anchored at gene promoters through long-range chromatin interactions, suggesting that ER-alpha functions by extensive chromatin looping to bring genes together for coordinated transcriptional regulation. We propose that chromatin interactions constitute a primary mechanism for regulating transcription in mammalian genomes.

ANNIE: integrated de novo protein sequence annotation.

Function prediction of proteins with computational sequence analysis requires the use of dozens of prediction tools with a bewildering range of input and output formats. Each of these tools focuses on a narrow aspect and researchers are having difficulty obtaining an integrated picture. ANNIE is the result of years of close interaction between computational biologists and computer scientists and automates an essential part of this sequence analytic process. It brings together over 20 function prediction algorithms that have proven sufficiently reliable and indispensable in daily sequence analytic work and are meant to give scientists a quick overview of possible functional assignments of sequence segments in the query proteins. The results are displayed in an integrated manner using an innovative AJAX-based sequence viewer. ANNIE is available online at: http://annie.bii.a-star.edu.sg. This website is free and open to all users and there is no login requirement.

Fusion transcripts and transcribed retrotransposed loci discovered through comprehensive transcriptome analysis using Paired-End diTags (PETs).

Identification of unconventional functional features such as fusion transcripts is a challenging task in the effort to annotate all functional DNA elements in the human genome. Paired-End diTag (PET) analysis possesses a unique capability to accurately and efficiently characterize the two ends of DNA fragments, which may have either normal or unusual compositions. This unique nature of PET analysis makes it an ideal tool for uncovering unconventional features residing in the human genome. Using the PET approach for comprehensive transcriptome analysis, we were able to identify fusion transcripts derived from genome rearrangements and actively expressed retrotransposed pseudogenes, which would be difficult to capture by other means. Here, we demonstrate this unique capability through the analysis of 865,000 individual transcripts in two types of cancer cells. In addition to the characterization of a large number of differentially expressed alternative 5' and 3' transcript variants and novel transcriptional units, we identified 70 fusion transcript candidates in this study. One was validated as the product of a fusion gene between BCAS4 and BCAS3 resulting from an amplification followed by a translocation event between the two loci, chr20q13 and chr17q23. Through an examination of PETs that mapped to multiple genomic locations, we identified 4055 retrotransposed loci in the human genome, of which at least three were found to be transcriptionally active. The PET mapping strategy presented here promises to be a useful tool in annotating the human genome, especially aberrations in human cancer genomes.

Global mapping of c-Myc binding sites and target gene networks in human B cells.

The protooncogene MYC encodes the c-Myc transcription factor that regulates cell growth, cell proliferation, cell cycle, and apoptosis. Although deregulation of MYC contributes to tumorigenesis, it is still unclear what direct Myc-induced transcriptomes promote cell transformation. Here we provide a snapshot of genome-wide, unbiased characterization of direct Myc binding targets in a model of human B lymphoid tumor using ChIP coupled with pair-end ditag sequencing analysis (ChIP-PET). Myc potentially occupies > 4,000 genomic loci with the majority near proximal promoter regions associated frequently with CpG islands. Using gene expression profiles with ChIP-PET, we identified 668 direct Myc-regulated gene targets, including 48 transcription factors, indicating that Myc is a central transcriptional hub in growth and proliferation control. This first global genomic view of Myc binding sites yields insights of transcriptional circuitries and cis regulatory modules involving Myc and provides a substantial framework for our understanding of mechanisms of Myc-induced tumorigenesis.

PET-Tool: a software suite for comprehensive processing and managing of Paired-End diTag (PET) sequence data.

BACKGROUND: We recently developed the Paired End diTag (PET) strategy for efficient characterization of mammalian transcriptomes and genomes. The paired end nature of short PET sequences derived from long DNA fragments raised a new set of bioinformatics challenges, including how to extract PETs from raw sequence reads, and correctly yet efficiently map PETs to reference genome sequences. To accommodate and streamline data analysis of the large volume PET sequences generated from each PET experiment, an automated PET data process pipeline is desirable. RESULTS: We designed an integrated computation program package, PET-Tool, to automatically process PET sequences and map them to the genome sequences. The Tool was implemented as a web-based application composed of four modules: the Extractor module for PET extraction; the Examiner module for analytic evaluation of PET sequence quality; the Mapper module for locating PET sequences in the genome sequences; and the Project Manager module for data organization. The performance of PET-Tool was evaluated through the analyses of 2.7 million PET sequences. It was demonstrated that PET-Tool is accurate and efficient in extracting PET sequences and removing artifacts from large volume dataset. Using optimized mapping criteria, over 70% of quality PET sequences were mapped specifically to the genome sequences. With a 2.4 GHz LINUX machine, it takes approximately six hours to process one million PETs from extraction to mapping. CONCLUSION: The speed, accuracy, and comprehensiveness have proved that PET-Tool is an important and useful component in PET experiments, and can be extended to accommodate other related analyses of paired-end sequences. The Tool also provides user-friendly functions for data quality check and system for multi-layer data management.

Multiplex sequencing of paired-end ditags (MS-PET): a strategy for the ultra-high-throughput analysis of transcriptomes and genomes.

The paired-end ditagging (PET) technique has been shown to be efficient and accurate for large-scale transcriptome and genome analysis. However, as with other DNA tag-based sequencing strategies, it is constrained by the current efficiency of Sanger technology. A recently developed multiplex sequencing method (454-sequencing) using picolitre-scale reactions has achieved a remarkable advance in efficiency, but suffers from short-read lengths, and a lack of paired-end information. To further enhance the efficiency of PET analysis and at the same time overcome the drawbacks of the new sequencing method, we coupled multiplex sequencing with paired-end ditagging (MS-PET) using modified PET procedures to simultaneously sequence 200,000 to 300,000 dimerized PET (diPET) templates, with an output of nearly half-a-million PET sequences in a single 4 h machine run. We demonstrate the utility and robustness of MS-PET by analyzing the transcriptome of human breast carcinoma cells, and by mapping p53 binding sites in the genome of human colorectal carcinoma cells. This combined sequencing strategy achieved an approximate 100-fold efficiency increase over the current standard for PET analysis, and furthermore enables the short-read-length multiplex sequencing procedure to acquire paired-end information from large DNA fragments.