Dataset Information

Composable languages for bioinformatics: the NYoSh experiment.

ABSTRACT: Language WorkBenches (LWBs) are software engineering tools that help domain experts develop solutions to various classes of problems. Some of these tools focus on non-technical users and provide languages to help organize knowledge while other workbenches provide means to create new programming languages. A key advantage of language workbenches is that they support the seamless composition of independently developed languages. This capability is useful when developing programs that can benefit from different levels of abstraction. We reasoned that language workbenches could be useful to develop bioinformatics software solutions. In order to evaluate the potential of language workbenches in bioinformatics, we tested a prominent workbench by developing an alternative to shell scripting. To illustrate what LWBs and Language Composition can bring to bioinformatics, we report on our design and development of NYoSh (Not Your ordinary Shell). NYoSh was implemented as a collection of languages that can be composed to write programs as expressive and concise as shell scripts. This manuscript offers a concrete illustration of the advantages and current minor drawbacks of using the MPS LWB. For instance, we found that we could implement an environment-aware editor for NYoSh that can assist the programmers when developing scripts for specific execution environments. This editor further provides semantic error detection and can be compiled interactively with an automatic build and deployment system. In contrast to shell scripts, NYoSh scripts can be written in a modern development environment, supporting context dependent intentions and can be extended seamlessly by end-users with new abstractions and language constructs. We further illustrate language extension and composition with LWBs by presenting a tight integration of NYoSh scripts with the GobyWeb system. The NYoSh Workbench prototype, which implements a fully featured integrated development environment for NYoSh is distributed at http://nyosh.campagnelab.org.

SUBMITTER: Simi M

PROVIDER: S-EPMC3898313 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Composable languages for bioinformatics: the NYoSh experiment.

Simi Manuele M Campagne Fabien F

PeerJ 20140102

Language WorkBenches (LWBs) are software engineering tools that help domain experts develop solutions to various classes of problems. Some of these tools focus on non-technical users and provide languages to help organize knowledge while other workbenches provide means to create new programming languages. A key advantage of language workbenches is that they support the seamless composition of independently developed languages. This capability is useful when developing programs that can benefit f ...[more]

PMID: 24482760

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Project description:The study of ancient protein sequences is increasingly focused on the analysis of older samples, including those of ancient hominins. The analysis of such ancient proteomes thereby potentially suffers from "cross-species proteomic effects": the loss of peptide and protein identifications at increased evolutionary distances due to a larger number of protein sequence differences between the database sequence and the analyzed organism. Error-tolerant proteomic search algorithms should theoretically overcome this problem at both the peptide and protein level; however, this has not been demonstrated. If error-tolerant searches do not overcome the cross-species proteomic issue then there might be inherent biases in the identified proteomes. Here, a bioinformatics experiment is performed to test this using a set of modern human bone proteomes and three independent searches against sequence databases at increasing evolutionary distances: the human (0 Ma), chimpanzee (6-8 Ma) and orangutan (16-17 Ma) reference proteomes, respectively.Incorrectly suggested amino acid substitutions are absent when employing adequate filtering criteria for mutable Peptide Spectrum Matches (PSMs), but roughly half of the mutable PSMs were not recovered. As a result, peptide and protein identification rates are higher in error-tolerant mode compared to non-error-tolerant searches but did not recover protein identifications completely. Data indicates that peptide length and the number of mutations between the target and database sequences are the main factors influencing mutable PSM identification.The error-tolerant results suggest that the cross-species proteomics problem is not overcome at increasing evolutionary distances, even at the protein level. Peptide and protein loss has the potential to significantly impact divergence dating and proteome comparisons when using ancient samples as there is a bias towards the identification of conserved sequences and proteins. Effects are minimized between moderately divergent proteomes, as indicated by almost complete recovery of informative positions in the search against the chimpanzee proteome (?90%, 6-8 Ma). This provides a bioinformatic background to future phylogenetic and proteomic analysis of ancient hominin proteomes, including the future description of novel hominin amino acid sequences, but also has negative implications for the study of fast-evolving proteins in hominins, non-hominin animals, and ancient bacterial proteins in evolutionary contexts.

Project description:Background and aimsinflammation plays an important role in atrial fibrillation (AF). In this study, we investigated the significance of immune cell infiltration in AF and identified the potential Hub genes involved in the regulation of immune cell infiltration in AF.Methodswe obtained AF datasets from the GEO database and analyzed them for obtaining differentially expressed genes (DEGs) by R software. Then, we performed GO, KEGG, and GSEA enrichment analyses of DEGs. The Hub genes of AF were determined by least absolute shrinkage selection operator (LASSO) regression analysis and weighted gene co-expression network analysis (WGCNA). Their validation was verified by using quantitative polymerase chain reaction (qPCR) in the AF rat model. Finally, we used a single sample GSEA (ssGSEA) to analyze immune cell infiltration and its relationship with hub genes.ResultsWe obtained 298 DGEs from the heatmap and found that DGEs were closely related to inflammation, immunity, and cytokine interactions by enrichment analyses. We obtained 10 co-expression modules by WGCNA. Among them, the module including CLEC4A, COTL1, EVI2B, FCER1G, GAPT, HCST, NCF2, PILRA, TLR8, and TYROBP had the highest correlation with AF. Four Hub genes (PILRA, NCF2, EVI2B, GAPT) were obtained further by LASSO analysis. The results suggested that the expression level of PILRA was significantly elevated in the rats with AF by qPCR, compared to the rats without AF. The results revealed that the infiltration of neutrophils, macrophages, monocytes, mast cells, immature B cells, myeloid-derived suppressor cell (MDSC), dendritic cell, and T cells and their partial subpopulations were closely related to AF by ssGSEA analysis, and PILRA was positively correlated with immature B cell, monocyte, macrophage, mast cell, dendritic cell, and T cells and their partial subpopulations by Spearman correlation analysis.ConclusionsPILRA was closely related to multiple types of immune cell infiltration, which may be associated with AF. PILRA may be a novel target of intervention for AF.

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Composable languages for bioinformatics: the NYoSh experiment.

Publications

Composable languages for bioinformatics: the NYoSh experiment.

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