Bioinformatics Software Tools Research Articles

MiRNA-BD: an evidence-based bioinformatics model and software tool for microRNA biomarker discovery

ABSTRACTMicroRNAs (miRNAs) are small non-coding RNAs with the potential as biomarkers for disease diagnosis, prognosis and therapy. In the era of big data and biomedical informatics, computer-aided biomarker discovery has become the current frontier. However, most of the computational models are highly dependent on specific prior knowledge and training-testing procedures, very few are mechanism-guided or evidence-based. To the best of our knowledge, untill now no general rules have been uncovered and applied to miRNA biomarker screening. In this study, we manually collected literature-reported cancer miRNA biomarkers and analyzed their regulatory patterns, including the regulatory modes, biological functions and evolutionary characteristics of their targets in the human miRNA-mRNA network. Two evidences were statistically detected and used to distinguish biomarker miRNAs from others. Based on these observations, we developed a novel bioinformatics model and software tool for miRNA biomarker discovery (http://sysbio.suda.edu.cn/MiRNA-BD/). In contrast to routine methods that focus on miRNA synergic functions, our method searches for vulnerable sites in the miRNA-mRNA network and considers the independent regulatory power of miRNAs, i.e., single-line regulations between miRNAs and mRNAs. The performance comparison demonstrates the generality and precision of our model, which identifies miRNA biomarkers for cancers as well as other complex diseases without training or specific prior knowledge.

Current Challenges in Plant Eco-Metabolomics.

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses

Biomedical data repositories such as the Gene Expression Omnibus (GEO) enable the search and discovery of relevant biomedical digital data objects. Similarly, resources such as OMICtools, index bioinformatics tools that can extract knowledge from these digital data objects. However, systematic access to pre-generated ‘canned’ analyses applied by bioinformatics tools to biomedical digital data objects is currently not available. Datasets2Tools is a repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets. The Datasets2Tools repository also contains the indexing of 4,901 published bioinformatics software tools, and all the analyzed datasets. Datasets2Tools enables users to rapidly find datasets, tools, and canned analyses through an intuitive web interface, a Google Chrome extension, and an API. Furthermore, Datasets2Tools provides a platform for contributing canned analyses, datasets, and tools, as well as evaluating these digital objects according to their compliance with the findable, accessible, interoperable, and reusable (FAIR) principles. By incorporating community engagement, Datasets2Tools promotes sharing of digital resources to stimulate the extraction of knowledge from biomedical research data. Datasets2Tools is freely available from: http://amp.pharm.mssm.edu/datasets2tools.

Uso de herramientas para alineación de secuencias y creación de árboles filogenéticos para la determinación de especies

El artículo presente se enfoca en las herramientas de software y algoritmos de Bioinformática que se utilizan en la determinación de especies para mejorar y obtener resultados con mayor precisión. Este proyecto se realizó en colaboración con el centro de investigación de la Universidad Iberoamericana de Ciencia y Tecnología, de Santiago de Chile. Con el uso de programas de software que ejecutan algoritmos para crear árboles filogenéticos, se determinó que una muestra de una especie desconocida no necesariamente pertenece a una especie conocida, aunque esta sea idéntica morfológicamente.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes.

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

Explore the diagnosis value of whole exome sequencing in pediatric neuro-developmental disorders

Objective To evaluate the application value of whole exome sequencing (WES) in diagnosis of NDDs (neuro-developmental disorders) children. Metheod WES was used for the diagnosis of 35 unexplained NDD children, which admitted to the outpatient and ward of Children′s hospital affiliated to Capital institute of pediatric from November 2015 to November 2016. These children′s clinical data was collected detailedly. Using bioinformatics software tools combining with patient′s phenotype, the candidate genetic/genomic variants of these patients were identified from WES data. The final pathogenicity of genetic/genomic variants was interpreted according to the guideline of the American College of Medical Genetics and Genomics (ACMG), meanwhile, the variants validation and co-separation analysis in the parents and their family members were performed by Sanger sequencing, real time-PCR and multiplex ligation-dependent probe amplification (MLPA). Results 14 pathogenic single nucleotide variants (SNVs) and three pathogenic copy number variations (CNVs) were detected in the 35 NDD children, the detection rate in this study is 48.6%. Among the 14 pathogenic SNVs, 11 of them are the definite NDD-related genes according to OMIM database (such as CHARGE syndrome, Wiedemann-Steiner syndrome, Cockayne syndrome, etc.), and six of them are de novo (6/11, 54.6%). Three pathogenic CNVs were identified from WES data, including two microduplications and one microdeletion. Meanwhile, a female child carrying a frame shift mutation in MECP2 was found and the germline mosaicism with low-frequency mutation of this site (8.4%) was confirmed by his father's sperm. Conclusions The diagnosis rate of WES in NDDs children is 48.6% in our small-sample study. In addition to pathogenic/likely pathogenic SNVs, CNVs can be detected successfully from WES data, which effectively improved the diagnosis yield in NDDs children. (Chin J Lab Med, 2017, 40: 623-629) Key words: Developmental disabilites; Exome; Sequence analysis, DNA; Polymorphism, single nucleotide; DNA copy number variations

Improving the performance of minimizers and winnowing schemes.

MotivationThe minimizers scheme is a method for selecting k-mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g. too many k-mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k-mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.ResultsWe provide an in-depth analysis of the effect of k-mer ordering on the performance of the minimizers technique. By using small universal hitting sets (a recently defined concept), we show how to significantly improve the performance of minimizers and avoid some of its worse behaviors. Based on these results, we encourage bioinformatics software developers to use an ordering based on a universal hitting set or, if not possible, a randomized ordering, rather than the lexicographic order. This analysis also settles negatively a conjecture (by Schleimer et al.) on the expected density of minimizers in a random sequence.Availability and ImplementationThe software used for this analysis is available on GitHub: https://github.com/gmarcais/minimizers.git.

Bioinformatics analysis on the differentiation of bone mesenchymal stem cells into osteoblasts and adipocytes.

The present study aimed to screen several differentially expressed genes (DEGs) and differentially expressed microRNAs (miRNAs) for two types of mesenchymal stem cell (MSC) differentiation. Bone morphogenetic protein 6 (BMP-6) and dexamethasone were used to induce MSCs towards osteoblastic differentiation or adipocytic differentiation. The t-test in the Bioconductor bioinformatics software tool was used to screen DEGs and differentially expressed miRNAs in the two samples. Subsequent gene ontology (GO) and pathway analyses on the DEGs were performed using the GO and Kyoto Encyclopedia of Genes and Genomes databases, respectively; potential target genes for the screened miRNAs were predicted using the TargetScan database. In addition, an interaction network between the DEGs and miRNAs was constructed. Numerous DEGs and miRNAs were screened during osteoblastic and adipocytic differentiation of MSCs. Important pathways, such as glutathione metabolism, pathogenic Escherichia coli infection and Parkinson's disease, and GO terms, including cytoskeletal protein binding and phospholipase inhibitor activity, were enriched in the screened DEGs from MSCs undergoing osteogenic differentiation and adipocytic differentiation. miRNAs, including miRNA (miR)-382 and miR-203, and DEGs, including neuronal growth regulator 1 (NEGR1), phosphatidic acid phosphatase 2B (PPAP2B), platelet-derived growth factor receptor alpha (PDGFRA), interleukin 6 signal transducer (IL6ST) and sortilin 1 (SORT1), were demonstrated to be involved in osteoblastic differentiation. In addition, the downregulated miRNAs (including miR-495, miR-376a and miR-543), the upregulated miR-106a, the upregulated DEGs, including enabled homolog (ENAH), polypeptide N-acetylgalactosaminyltransferase 1 and acyl-CoA synthetase long-chain family member 1, and the downregulated repulsive guidance molecule family member B and semaphorin SEMA7A were demonstrated to be involved in adipocytic differentiation. The results of the present study suggested that miRNAs (miR-203 and miR-382) and DEGs (NEGR1, PPAP2B, PDGFRA, IL6ST and SORT1) may serve pivotal functions in the osteoblastic differentiation of MSCs, whereas miR-495, which is also involved in osteoblast differentiation and had four targets, including NEGR1, miR-376a, miR-543 and ENAH may have crucial roles in adipocytic differentiation of MSCs.

Target regulation of micro-RNA-203 to the expression of collagen type Ⅳ alpha 4 and its role in oral submucous fibrosis

To explore the effect of microRNA-203(miR-203) on the expression of collagen type IV alpha 4(COL4A4) and its role in oral submucous fibrosis(OSF). It has been revealed that COL4A4 is the putative target of miR-203 by retrieving a variety of bioinformatics software tools. The expression of COL4A4 protein in the indicated tissues was examined by Western blotting analysis and the potential binding sites of miR-203 and COL4A4-3'UTR was analyzed by using bioinformatics. This study integrated a fragment of COL4A4 3'UTR containing the target sequence into the pMIR-REPORT luciferase vector. Then, the COL4A4 3'UTR-luciferase and miR-203 were contransfected into 293T cells. Beta-gal was used as a control to monitor transfection efficiency. Cells were harvested and luciferase activity was measured after 24 h of transfection. The relative expression mean valves of COL4A4 were 2.46 ± 2.26 in OSF and 0.70 ± 0.49 in normal control. The expression of COL4A4 in OSF was higher than normal control(P<0.05) and the difference between groups was statistically significant. The relative expression mean valves of miR-203 were 2.57 ± 2.09 in OSF and 154.07 ± 191.11 in normal control. The expression of miR-203 in OSF was lower than in normal control(P<0.01) and the difference between groups was statistically significant. Bioinformatics analysis revealed that the COL4A4 3'UTR had a conserved site (site 1) and 3 non-conserved sites(site 2, site 3, site4) complementary to the miR-203. MiR-203 could significantly inhibit site 1-luciferase reporter activity, partially inhibited site 3- and site 4-luciferase reporter activities, while no significant effect on site 3-luciferase reporter activity. MiR-203 was negatively correlated with the expression of COL4A4 protein in OSF. Moreover, miR-203 repressed the expression of COL4A4 by targeting 3'UTR site of COL4A4 mRNA.

Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP

BackgroundTherecent development and availability of different genotype by sequencing (GBS) protocols provided a cost-effective approach to perform high-resolution genomic analysis of entire populations in different species. The central component of all these protocols is the digestion of the initial DNA with known restriction enzymes, to generate sequencing fragments at predictable and reproducible sites. This allows to genotype thousands of genetic markers on populations with hundreds of individuals. Because GBS protocols achieve parallel genotyping through high throughput sequencing (HTS), every GBS protocol must include a bioinformatics pipeline for analysis of HTS data. Our bioinformatics group recently developed the Next Generation Sequencing Eclipse Plugin (NGSEP) for accurate, efficient, and user-friendly analysis of HTS data.ResultsHere we present the latest functionalities implemented in NGSEP in the context of the analysis of GBS data. We implemented a one step wizard to perform parallel read alignment, variants identification and genotyping from HTS reads sequenced from entire populations. We added different filters for variants, samples and genotype calls as well as calculation of summary statistics overall and per sample, and diversity statistics per site. NGSEP includes a module to translate genotype calls to some of the most widely used input formats for integration with several tools to perform downstream analyses such as population structure analysis, construction of genetic maps, genetic mapping of complex traits and phenotype prediction for genomic selection. We assessed the accuracy of NGSEP on two highly heterozygous F1 cassava populations and on an inbred common bean population, and we showed that NGSEP provides similar or better accuracy compared to other widely used software packages for variants detection such as GATK, Samtools and Tassel.ConclusionsNGSEP is a powerful, accurate and efficient bioinformatics software tool for analysis of HTS data, and also one of the best bioinformatic packages to facilitate the analysis and to maximize the genomic variability information that can be obtained from GBS experiments for population genomics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2827-7) contains supplementary material, which is available to authorized users.

Transcription factor c-jun regulates β3Gn-T8 expression in gastric cancer cell line SGC-7901

Aberrant glycosylation, a common feature of malignant alteration, is partly due to changes in the expression of glycosyltransferases, including β1,3-N-acetyl-glucosaminyltrans-ferase8 (β3Gn‑T8), which synthesizes poly-N-acetyllactosamine (poly-LacNAc) chains on β1,6 branched N‑glycans. Although the role of β3Gn‑T8 in tumors has been reported, the regulation of β3Gn‑T8 expression, however, is still poorly understood. In the present study, we used three online bioinformatic software tools to identify multiple c‑jun binding sites in the promoter of the β3Gn‑T8 gene. Using luciferase reporter assay, chromatin immunoprecipitation (ChIP) analysis, RT‑PCR and western blot analysis, we revealed that c‑jun could bind to and activate the β3Gn‑T8 promoter, thus upregulating β3Gn‑T8 expression. This was also confirmed by changes in β3Gn‑T8 activity as demonstrated by flow cytometry, immunofluorescence and lectin blot analysis using LEA lectin. Moreover, expression of glycoprotein HG‑CD147, the substrate of β3Gn‑T8, was also regulated by c‑jun. In addition, c‑jun and β3Gn‑T8 were more highly expressed in the gastric cancer tissues when compared to these levels in the adjacent non‑tumor gastric tissues, and β3Gn‑T8 expression was positively correlated with c‑jun expression. These results suggest that c‑jun plays a significant role in regulating the expression of β3Gn‑T8 in the SGC‑7901 cell line and may be involved in the development of malignancy via the activity of β3Gn‑T8.

Mass Spectrometry-Based Protein Quantification.

Quantification of individual proteins and even entire proteomes is an important theme in proteomics research. Quantitative proteomics is an approach to obtain quantitative information about proteins in a sample. Compared to qualitative or semi-quantitative proteomics, this approach can provide more insight into the effects of a specific stimulus, such as a change in the expression level of a protein and its posttranslational modifications, or to a panel of proposed biomarkers in a given disease state. Proteomics methodologies, along with a variety of bioinformatics approaches, are a major tool in quantitative proteomics. As the theory and technological aspects underlying the proteomics methodologies will be extensively described in Chap. 20 , and protein identification as a prerequisite of quantification has been discussed in Chap. 17 , we will focus on the quantitative proteomics bioinformatics algorithms and software tools in this chapter. Our goal is to provide researchers and newcomers a rational framework to select suitable bioinformatics tools for data analysis, interpretation, and integration in protein quantification. Before doing so, a brief overview of quantitative proteomics is provided.

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data

Motivation: Detection of random errors and systematic biases is a crucial step of a robust pipeline for processing high-throughput sequencing (HTS) data. Bioinformatics software tools capable of performing this task are available, either for general analysis of HTS data or targeted to a specific sequencing technology. However, most of the existing QC instruments only allow processing of one sample at a time.Results: Qualimap 2 represents a next step in the QC analysis of HTS data. Along with comprehensive single-sample analysis of alignment data, it includes new modes that allow simultaneous processing and comparison of multiple samples. As with the first version, the new features are available via both graphical and command line interface. Additionally, it includes a large number of improvements proposed by the user community.Availability and implementation: The implementation of the software along with documentation is freely available at http://www.qualimap.org.Contact: meyer@mpiib-berlin.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.

Abstract 1097: Knockdown of splicing factor ESRP1 affects multiple splicing factors

Abstract Introduction: Tissue-specific alternative splicing (AS) is an important mechanism for regulating gene expression. Epithelial Splicing Regulatory Proteins 1 and 2 (ESRP1 and ESRP2), two RNA-binding proteins (RBPs) that promote splicing, are potential candidates that contribute to breast cancer recurrence and resistance to therapies. In our prior studies, we have shown that ESRPs are associated with endocrine resistance. In this study, we seek to investigate the impact of the ESRP1 knockdown in endocrine resistant breast cancer cells. Methods: Expression of ESRP1 was analyzed in endocrine resistant-LCC2 and LCC9 cells. Knockdown of ESRP1 expression in LCC2 and LCC9 cells was achieved using lenti-viral based shRNA approach (Mission TRC human shRNA constructs, Sigma). We next performed paired-end RNA-sequencing of LCC2 and LCC9 cells transfected with control and two clones representative of shRNA knockdown (Illumina HiSeq platform, SeqWright Genomic Services) according to the manufacturer's instructions. After processing and quality check of raw FASTQ data (Illumina CASAVA, FASTQ and FASTX toolkits), reads were aligned, and quantified using bioinformatics software tools (Tophat/Cufflinks and Sailfish). Results: Expression of ESRP1 is significantly elevated in ERα-positive endocrine -resistant cells (LCC2 and LCC9 cells). ESRP1 knockdown was confirmed using qRT-PCR analysis in LCC2 cell lines (clone1 - 80% reduction and clone 3- 95% reduction) and in LCC9 cells (clone 2- 90% reduction and clone 3- 50% reduction). RNA-seq analysis identified 5153 differentially regulated genes between ESRP1 knockdown and control LCC2 cell lines. Of the 5153 genes, 1117 and 2109 genes were unique to clone 1 and clone 3, respectively, while 1927 genes were common in both clones. Targeted RNA-seq analysis also revealed that ESRP1 regulates the expression of several genes including other splicing factors. Furthermore, a functional enrichment analysis of these genes revealed a possible role of altering important pathways such as cell-cell adhesion and cell signaling. Additionally, it was observed that neither ESR1 nor any of its downstream genes were affected by the knockdown of ESRP1. This data was similar to that obtained in knockdown of LCC9 cells. Conclusions: ESRP1 regulates splicing of multiple genes in ER+ breast cancer. The process is independent of ER pathway. Targeting splicing could be a novel modality for treating endocrine therapy resistant breast cancer. Citation Format: Yesim Gökmen-Polar, Yaseswini Neelamraju, Xiaoping Gu, Gouthami Nallamothu, Sarath C. Janga, Sunil Badve. Knockdown of splicing factor ESRP1 affects multiple splicing factors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1097. doi:10.1158/1538-7445.AM2015-1097

O4. Genome wide sequencing approaches to identify missing heritability of preeclampsia

Introduction Genome-wide mapping approaches have been successfully used to identify several candidate susceptibility genes for preeclampsia (PE). Of note is the identification of the ACVR2A gene on chromosome 2 that was originally identified by our group by linkage and fine mapping studies in a cohort of 34 multiplex preeclampsia families from Australia and New Zealand. Genetic association of preeclampsia risk to variants in or near this gene has now been validated in three independent population studies from Norway, Turkey and Brazil. Most recently, a Dutch study has demonstrated a plausible functional role in trophoblast invasion/spiral artery remodelling for the ACVR2A SNP variant showing strongest association. Despite these successes there remains a substantial portion of the heritable risk of PE unaccounted for. We propose that family-based sequencing is an optimal study design for the identification of potentially rare novel risk variants of moderate to large effect. Objective To identify the missing heritability of PE risk in the cohort of 34 Australian/New Zealand families previously used in linkage mapping studies by our group. Methods Whole exome and whole genome DNA sequencing using Illumina and Life Technologies massively parallel DNA sequencing platforms. We have completed whole exome sequencing in the entire 34 family cohort and are currently doing whole genome sequencing in a subset, representing the largest five families. Results Our preliminary bioinformatic interrogation has identified 428,229 variants from targeted exonic sequencing and 68,933 of these are non-synonymous (i.e., a nucletide substitution that changes the amino acid sequence of a protein), with 15,024 of these variants being determined to be deleterious using specific bioinformatic software tools including SIFT ( ⩽ 0.05) and Polyphen2 ( ⩾ 0.453). A total of 3664 of these non-synonymous deleterious variants are detectable in more than four individuals indicating a high potential for identifying rare functional variants impacting PE. Conclusion We are currently applying linkage, association and pathway lineage specific filtering on this data in an effort to identify rare risk variants segregating/shared among affected and non-affected family members. We are initially focusing on deleterious non-synonymous coding sequence variants and variants in the most likely regulatory regions of genes.

Using Different Bioinformatics Software Tools for Determining the Carbon Dioxide (CO2) Dependence in Staphhylococcus aureus

Staphylococcus aureus (S. aureus) is a Gram-positive bacterium that occurs in microscopic bunches similar to grapes. In general, S. aureus shows two different colonies; on agar dishes S. aureus forms huge golden yellow colonies, while on blood agar dishes it has haemolytic properties. Recently, different major studies have discovered CO2-dependent S. aureus strains that can be found as an inhabitant of small colony variants (SCV) S. aureus, which can cause recurrent and persistent infections. Although it has been investigated the mechanism of this CO2 dependence, this paper aims to use different bioinformatics software tools, such as MUMmer, BLAST, Jalview and ClustalW, in order to compare and search for the differences in the enzymes involved in pathways that can be altered in the CO2-dependent S. aureus strain. That is, the entire genome of CO2-dependent S. aureus strain was compared against another strain, not CO2-dependent using the software tool MUMmer. Based on this tool, the results show that the plots of the CO2-dependent strain against the COL and Mu50 strains did not show any gaps, while the connected thick diagonal line reveals strong similarities between the sequences in comparison to the thin diagonal line. Surprisingly, the results of using BLAST alignment tool did not illustrate any obvious mutations. Besides, the comparison between a number of strains and the CO2-dependent S. aureus strain was carried out using the ClustalW alignment tool and Jalview software; consequently it did not show any observable mutations, as well.

Editorial from the Editor-in-Chief

The EiC reviews his first year of serving as the Editor-in-Chief (EIC) of the IEEE/ACM Transactions on Computational Biology and Bioinformatics. In 2013, two new columns were started in the journal, one on visionary perspectives by lead authors of the field and one on bioinformatics software tools and databases. Last year, TCBB received a total of 419 submissions, and published 139 papers, including 30 best papers selected from six bioinformatics conferences. Seventeen new associate editors joined the Editorial Board of TCBB in 2013. They were Professor Pierre Baldi of UC Irvine, Professor Graziano Chesi of Hongkong University, Professor Charlie Hodgman of the University of Nottingham, Professor Vasant Honavar of Penn State University, Dr. Suzanna Lewis of Lawrence Berkley National Lab, Professor Tianming Liu of the University of Georgia, Professor Yixue Li of the Chinese Academy of Sciences ? Shanghai, Professor Michael Lutz of Duke University, Professor Bin Ma of Waterloo University, Dr. Sergei Maslov of Brookhaven National Lab, Professor Jason Moore of Dartmouth College, Professor Sanjay Ranka of the University of Florida, Professor Haixu Tang of Indiana University, Professor Yufeng Wu of the University of Connecticut, Professor Dong Xu of the University of Missouri, Professor Ming Zhan of The Methodist Hospital Research Institute, and Professor Yang Zhang of the University of Michigan. Among them, Professor Dong Xu was appointed as the Associate Editor-in-Chief in charge of special issues in the journal. Biographies for these associate editors are provided.

Mirna Expression Profiling and Proteomic Analysis Of Circulating Exosomes From Multiple Myeloma Patients

IntroductionExosomes are small vesicles (50-100 nm) of endocytic origin, which are released in the extra-cellular milieu by several cell types. It is known that cell-to-cell communication is partially mediated by exosomes. Exosomes play a role in tumor progression where they have been shown to carry and transfer microRNAs (miRNAs) and proteins to the recipient cells. In this study, we sought to characterize circulating exosomes in terms of their ability to modulate the microenvironment, leading to Multiple Myeloma (MM) progression. MethodExosomes were collected from peripheral blood obtained from healthy individuals (n=5), MGUS patients (n=5) and MM patients (n=10), using ultracentrifugation. Further characterization was carried out using electron microscopy and immunogold labeling for the detection of CD63 and CD81 and for the size using Nanosight® analysis. MiRNA were isolated using miRNeasy mini kit (Qiagen®) and profiling has been performed using nCounter miRNA expression assay (Nanostring® Technologies, Seattle WA). Bioinformatic software tools (TargetScan, MIRDB) were used to predict the target genes of identified miRNA to define their function. Proteins were isolated from exosomes following lysis and precipitated by acetone before in-solution trypsin digestion and ZipTip® purification. Proteomic analysis was performed using mass spectrometry (BIDMC Mass Spectrometry, ObiTrap Elite®). Spectral count numbers were determined with a false discovery rate (FDR) less than 0.5%. ResultsCirculating exosomes were studied at ultrastructural level showing positivity for CD81 and CD63, as demonstrated by immunogold labeling and electron microscopy. Exosome number and size did not differ based on clinical stage on Nanosight® analysis. We identified 16 miRNAs differentially expressed in circulating exosomes obtained from MGUS patients compared to healthy subjects (FC >2 or <-2; p<0.05): specifically, higher expression of miR-450a, -30e, -125a, -300 and lower expression of miR-185, -150, -98 were observed in MGUS- compared to healthy individual-derived circulating exosomes. Interestingly, miR-30e and -150 modulate NK cell activity by targeting perforin and c-Myb, respectively. We found 96 miRNAs differentially expressed in circulating exosomes from MM patients as compared to healthy donors: specifically lower expression of Let-7 family members, miR-150, -15a and higher expression of miR-125b, -144 and -363 were observed. Interestingly, miR-15a is involved in angiogenesis regulating VEGFA and FGF2. Let-7 family members are tumor suppressors targeting k-Ras and c-Myc and miR-150 regulates CXCR-4 expression. Moreover, these patterns have been described in MM cells suggesting that circulating exosomes in MM are mainly released from MM cells and could play a role in modulating the tumor micro-environment. The mass spectrometry analysis was performed on protein derived from circulating exosomes from 5 healthy donors, 5 MGUS and 10 MM patients. 272 proteins were identified in circulating exosomes including proteins highly associated with exosomes such as CD9, HSP70, Rab proteins (Rab7a; Rab5; Rab27b) and annexins. Comparing MM exosomal proteins to healthy donor exosomal proteins, we found significantly distinctive peptide counts for fibronectin (FC=3.5; p=0.002), AMBP protein (FC=3; p=0.001) and Ig gamma-1 chain C region (FC=2.5; p=0.006). Interestingly, fibronectin expression level in the microenvironment has been reported to be associated with tumor proliferation and drug resistance in MM. ConclusionThese findings indicate that circulating exosomes differ between normal, MGUS and MM patients in terms of miRNA and protein content. Circulating exosomes could potentially be involved in modulating the host microenvironment for specific homing of clonal plasma cells to the bone marrow; thus providing a better understanding of the epigenetic changes responsible for the transition to MM stage. Disclosures:Leleu:CELGENE: Honoraria; JANSSEN: Honoraria. Ghobrial:Onyx: Advisoryboard Other; BMS: Advisory board, Advisory board Other, Research Funding; Noxxon: Research Funding; Sanofi: Research Funding.

Using HPC for teaching and learning bioinformatics software: Benefits and challenges

Background We present our work on using the XSEDE high-performance computing (HPC) network to support and facilitate hands-on bioinformatics tasks for participants of our Essentials of Next Generation Sequencing (NGS) workshop, as well as for students and other learners. In the summer of 2012, the University of Kentucky hosted the NGS workshop, attended by faculty and students from across the Commonwealth who were introduced to the laboratory and bioinformatic components of next-generation sequencing and sequence analysis. Participants used next-generation technology to sequence real genetic material, then used a variety of bioinformatics software tools to assemble those sequences, compare and align them to other sequences, predict genes, and visualize the genome. Due to the success of the 2012 workshop, the second workshop, planned for this summer, is expected to be larger in scale and to include even more participants. It will furthermore include several additional bioinformatics tools and tasks. Since participants will be simultaneously running intensive bioinformatics computing tasks, the resources required will exceed the capacity of the single twelve-core server used to support the workshop last year. One particular resource that appears promising to meet our intensive computational needs is the XSEDE grid computing network, a follow-on to the TeraGrid project designed specifically for “e-Science” and scientific computing. Many of the systems in the XSEDE network already support some of the software used within our workshop; however, many of the programs we will demonstrate have not previously been installed on tested on the XSEDE network. We will describe our experiences porting these applications to, and deploying them on, XSEDE. We will also discuss the challenges that the HPC approach presents for teaching and learning, particularly the complexities of navigating between time-sharing systems and remote job scheduling.