Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 Workshop report

Perez‐Riverol, Yasset; Hermjakob, Henning; Kohlbacher, Oliver; Martens, Lennart; Creasy, David M.; Cox, Jürgen; Leprevost, Felipe V.; Shan, Baozhen; Pérez‐Nueno, Violeta I.; Blazejczyk, Michal; Punta, Marco; Vierlinger, Klemens; Valienten, Pedro A.; León, Kalet; Chinea, Glay; Guirola, Osmany; Bringas, Ricardo; Cabrera, Gleysin; Guillén, Gerardo; Padrón, Gabrìel; González, Luís Javier Durán; Besada, Vladimir

doi:10.1016/j.jprot.2013.01.019

Cited by 19 publications

(16 citation statements)

References 41 publications

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“…5). Unlike genomics, the under-developed tools for proteomic analysis have many pitfalls [149–151]. However, use of databases such as NCBI, UniProtKB, Swiss-Prot, Protein Information resource (PIR) and OWL, has streamlined protein identification pipeline (Table 4).…”

Section: Proteomics and Metabolomics In Pulmonary Biologymentioning

confidence: 99%

Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

Pathak¹,

Davé

2014

Cell Physiol Biochem

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Assimilation and integration of “omics” technologies, including genomics, epigenomics, proteomics, and metabolomics has readily altered the landscape of medical research in the last decade. The vast and complex nature of omics data can only be interpreted by linking molecular information at the organismic level, forming the foundation of systems biology. Research in pulmonary biology/medicine has necessitated integration of omics, network, systems and computational biology data to differentially diagnose, interpret, and prognosticate pulmonary diseases, facilitating improvement in therapy and treatment modalities. This review describes how to leverage this emerging technology in understanding pulmonary diseases at the systems level -called a “systomic” approach. Considering the operational wholeness of cellular and organ systems, diseased genome, proteome, and the metabolome needs to be conceptualized at the systems level to understand disease pathogenesis and progression. Currently available omics technology and resources require a certain degree of training and proficiency in addition to dedicated hardware and applications, making them relatively less user friendly for the pulmonary biologist and clinicians. Herein, we discuss the various strategies, computational tools and approaches required to study pulmonary diseases at the systems level for biomedical scientists and clinical researchers.

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Section: Proteomics and Metabolomics In Pulmonary Biologymentioning

confidence: 99%

Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

Pathak¹,

Davé

2014

Cell Physiol Biochem

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“…Bottom-up proteomics is currently the standard analytical method to identify and quantify proteins based on the presence of peptides obtained by digestion of the protein mix during sample preparation. Current computational approaches can typically be broken down into three main steps: 1) peptide identification, 2) quality assessment of the peptide identifications, and 3) the assembly of the identified peptides into a final protein list using protein inference algorithms (7,8). During peptide identification, peptide fragmentation spectra (MS/MS) are assigned to peptide sequences to generate a set of Peptide-Spectrum Matches (PSMs) using database search engines, such as Mascot (9), MS-GF+ (10), or X!Tandem (11).…”

Section: Introductionmentioning

confidence: 99%

In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics

Audain

Uszkoreit

Sachsenberg

et al. 2017

Journal of Proteomics

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“…However, despite the vast improvement in biological data visualisation tools and libraries, two major challenges remain at present: How to benefit from this multi‐scale complex data without being overwhelmed . Clear objectives are needed to drive the design process in order to truly benefit from the visualisation. The advances in visualisation are not adequately described and shared within the biological scientific community . Without the help of the visualisation practitioners, it can be a daunting task for scientists to determine the best visualisation option among the vast range of choices. …”

Section: Introductionmentioning

confidence: 99%

Open source libraries and frameworks for biological data visualisation: A guide for developers

et al. 2015

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Recent advances in high-throughput experimental techniques have led to an exponential increase in both the size and the complexity of the data sets commonly studied in biology. Data visualisation is increasingly used as the key to unlock this data, going from hypothesis generation to model evaluation and tool implementation. It is becoming more and more the heart of bioinformatics workflows, enabling scientists to reason and communicate more effectively. In parallel, there has been a corresponding trend towards the development of related software, which has triggered the maturation of different visualisation libraries and frameworks. For bioinformaticians, scientific programmers and software developers, the main challenge is to pick out the most fitting one(s) to create clear, meaningful and integrated data visualisation for their particular use cases. In this review, we introduce a collection of open source or free to use libraries and frameworks for creating data visualisation, covering the generation of a wide variety of charts and graphs. We will focus on software written in Java, JavaScript or Python. We truly believe this software offers the potential to turn tedious data into exciting visual stories.

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Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 Workshop report

Cited by 19 publications

References 41 publications

Integrating <b><i>Omics</i></b> Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

Integrating <b><i>Omics</i></b> Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics

Open source libraries and frameworks for biological data visualisation: A guide for developers

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