scholarly journals Bioinformatics Methods and Tools to Advance Clinical Care

2015 ◽  
Vol 24 (01) ◽  
pp. 170-173 ◽  
Author(s):  
T. Lecroq ◽  
L. F. Soualmia ◽  
Keyword(s):  
Mass Spectrometry ◽  
Large Scale ◽  
Cancer Survival ◽  
High Resolution Mass Spectrometry ◽  
Clinical Care ◽  
Evaluation Process ◽  
Daily Practice ◽  
High Coverage ◽  
Research Activities ◽  
Translational Informatics

Summary Objectives: To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain and clinical care.Method: We provide a synopsis of the articles selected for the IMIA Yearbook 2015, from which we attempt to derive a synthetic overview of current and future activities in the field. As last year, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor has evaluated separately the set of 1,594 articles and the evaluation results were merged for retaining 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Bioinformatics and Translational Informatics yielded four excellent articles regarding data management and genome medicine that are mainly tool-based papers. In the first article, the authors present PPISURV a tool for uncovering the role of specific genes in cancer survival outcome. The second article describes the classifier PredictSNP which combines six performing tools for predicting disease-related mutations. In the third article, by presenting a high-coverage map of the human proteome using high resolution mass spectrometry, the authors highlight the need for using mass spectrometry to complement genome annotation. The fourth article is also related to patient survival and decision support. The authors present datamining methods of large-scale datasets of past transplants. The objective is to identify chances of survival. Conclusions: The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care. Indeed, there is a need for powerful tools for managing and interpreting complex, large-scale genomic and biological datasets, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts contribute to the challenge of impacting clinically the obtained results towards a personalized medicine.

scholarly journals Findings from the Section on Bioinformatics and Translational Informatics

2017 ◽  
Vol 26 (01) ◽  
pp. 188-192 ◽  
Author(s):  
H. Dauchel ◽  
T. Lecroq
Keyword(s):  
Intelligent Systems ◽  
Large Scale ◽  
Cancer Genomics ◽  
Clinical Care ◽  
Evaluation Process ◽  
Omics Data ◽  
Health Domain ◽  
Medical Genomics ◽  
Research Activities ◽  
Translational Informatics

Summary Objective: To summarize excellent current research and propose a selection of best papers published in 2016 in the field of Bioinformatics and Translational Informatics with applications in the health domain and clinical care. Methods: We provide a synopsis of the articles selected for the IMIA Yearbook 2017, from which we attempt to derive a synthetic overview of current and future activities in the field. As in 2016, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section coverage. Each section editor evaluated separately the set of 951 articles returned and evaluation results were merged for retaining 15 candidate best papers for peer-review. Results: The selection and evaluation process of papers published in the Bioinformatics and Translational Informatics field yielded four excellent articles focusing this year on the secondary use and massive integration of multi-omics data for cancer genomics and non-cancer complex diseases. Papers present methods to study the functional impact of genetic variations, either at the level of the transcription or at the levels of pathway and network. Conclusions: Current research activities in Bioinformatics and Translational Informatics with applications in the health domain continue to explore new algorithms and statistical models to manage, integrate, and interpret large-scale genomic datasets. As addressed by some of the selected papers, future trends would include the question of the international collaborative sharing of clinical and omics data, and the implementation of intelligent systems to enhance routine medical genomics.

scholarly journals Managing Large-Scale Genomic Datasets and Translation into Clinical Practice

2014 ◽  
Vol 23 (01) ◽  
pp. 212-214 ◽  
Author(s):  
L. F. Soualmia ◽  
T. Lecroq ◽  
Keyword(s):  
Large Scale ◽  
Genomic Data ◽  
Evaluation Process ◽  
Daily Practice ◽  
Supervised Machine Learning ◽  
Complex Data ◽  
Genome Medicine ◽  
Tumor Subtypes ◽  
Research Activities ◽  
Translational Informatics

Summary Objective:To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain. Method: We provide a synopsis of the articles selected for the IMIA Yearbook 2014, from which we attempt to derive a synthetic overview of current and future activities in the field. A first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor evaluated independently the set of 1,851 articles and 15 articles were retained for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Bioinformatics and Translational Informatics yielded three excellent articles regarding data management and genome medicine. In the first article, the authors present VEST (Variant Effect Scoring Tool) which is a supervised machine learning tool for prioritizing variants found in exome sequencing projects that are more likely involved in human Mendelian diseases. In the second article, the authors show how to infer surnames of male individuals by crossing anonymous publicly available genomic data from the Y chromosome and public genealogy data banks. The third article presents a statistical framework called iCluster+ that can perform pattern discovery in integrated cancer genomic data. This framework was able to determine different tumor subtypes in colon cancer. Conclusions: The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on large-scale biological, genomic, and Electronic Health Records data. Indeed, there is a need for powerful tools for managing and interpreting complex data, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts are contributing to the challenge of impacting clinically the results and even going towards a personalized medicine in the near future.

scholarly journals Findings from the Section on Bioinformatics and Translational Informatics

2016 ◽  
Vol 25 (01) ◽  
pp. 207-210
Author(s):  
T. Lecroq ◽  
H. Dauchel ◽  
Keyword(s):  
Large Scale ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Genomic Data ◽  
Evaluation Process ◽  
The Cancer Genome Atlas ◽  
Sub Saharan Africa ◽  
Health Domain ◽  
Research Activities ◽  
Translational Informatics

Summary Objectives : To summarize excellent current research and propose a selection of best papers published in 2015 in the field of Bioinformatics and Translational Informatics with application in the health domain and clinical care. Method : We provide a synopsis of the articles selected for the IMIA Yearbook 2016, from which we attempt to derive a synthetic overview of current and future activities in the field. As last year, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor has evaluated separately the set of 1,566 articles and the evaluation results were merged for retaining 14 articles for peer-review. Results : The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded four excellent articles focusing this year on data management of large-scale datasets and genomic medicine that are mainly new method-based papers. Three articles explore the high potential of the re-analysis of previously collected data, here from The Cancer Genome Atlas project (TCGA) and one article presents an original analysis of genomic data from sub-Saharan Africa populations. Conclusions : The current research activities in Bioinformatics and Translational Informatics with application in the health domain continues to explore new algorithms and statistical models to manage and interpret large-scale genomic datasets. From population wide genome sequencing for cataloging genomic variants to the comprehension of functional impact on pathways and molecular interactions regarding a given pathology, making sense of large genomic data requires a necessary effort to address the issue of clinical translation for precise diagnostic and personalized medicine.

scholarly journals Findings from the Section on Bioinformatics and Translational Informatics

2017 ◽  
Vol 26 (01) ◽  
pp. 188-191
Author(s):  
H. Dauchel ◽  
T. Lecroq
Keyword(s):  
Intelligent Systems ◽  
Large Scale ◽  
Cancer Genomics ◽  
Clinical Care ◽  
Evaluation Process ◽  
Omics Data ◽  
Health Domain ◽  
Medical Genomics ◽  
Research Activities ◽  
Translational Informatics

Summary Objective: To summarize excellent current research and propose a selection of best papers published in 2016 in the field of Bioinformatics and Translational Informatics with applications in the health domain and clinical care. Methods: We provide a synopsis of the articles selected for the IMIA Yearbook 2017, from which we attempt to derive a synthetic overview of current and future activities in the field. As in 2016, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section coverage. Each section editor evaluated separately the set of 951 articles returned and evaluation results were merged for retaining 15 candidate best papers for peer-review. Results: The selection and evaluation process of papers published in the Bioinformatics and Translational Informatics field yielded four excellent articles focusing this year on the secondary use and massive integration of multi-omics data for cancer genomics and non-cancer complex diseases. Papers present methods to study the functional impact of genetic variations, either at the level of the transcription or at the levels of pathway and network. Conclusions: Current research activities in Bioinformatics and Translational Informatics with applications in the health domain continue to explore new algorithms and statistical models to manage, integrate, and interpret large-scale genomic datasets. As addressed by some of the selected papers, future trends would include the question of the international collaborative sharing of clinical and omics data, and the implementation of intelligent systems to enhance routine medical genomics.

scholarly journals Efficient Results in Semantic Interoperability for Health Care

2016 ◽  
Vol 25 (01) ◽  
pp. 184-187
Author(s):  
J. Charlet ◽  
L. F. Soualmia ◽  
Keyword(s):  
Health Care ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Large Scale ◽  
Recommendation System ◽  
Clinical Care ◽  
Knowledge Bases ◽  
Semantic Interoperability ◽  
The Third ◽  
Research Activities

Summary Objectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowd-based method for ontology engineering. Conclusions: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

scholarly journals From Genome Sequencing to Bedside

2013 ◽  
Vol 22 (01) ◽  
pp. 175-177 ◽  
Author(s):  
L. F. Soualmia ◽  
T. Lecroq
Keyword(s):  
Genome Sequencing ◽  
High Throughput Sequencing ◽  
Health Management ◽  
Complex Diseases ◽  
Evaluation Process ◽  
Blood Analysis ◽  
Sequencing Technologies ◽  
Research Activities ◽  
Rare Gene ◽  
Translational Informatics

Summary Objectives: To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain and evidence-based medicine. Method: We provide a synopsis of the articles selected for the IMIA Yearbook 2013, from which we attempt to derive a synthetic overview of current and future activities in the field. Three steps of selection were performed by querying PubMed and Web of Science. A first set of 5,549 articles was refined into a second set of 1,272 articles from which 15 articles were retained for peer-review. Results: The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded four excellent articles regarding the Human Genome and Medicine. Exploiting genomic data depends on having the appropriate reference annotation available. In the first article, the goal of the GENCODE Consortium is to produce and publish The GENCODE human reference gene set. As a result it is composed by merged manual and automatic annotations, which are frequently updated from public experimental databases. The quality of genome sequencing is platform-dependant. In the second article, a generic database independent from the sequencing technologies, Huvariome, can help to identify errors and inconsistencies in sequencing. To understand complex diseases of patients it will be of great importance to detect rare gene variants. This is the aim of the third study. Finally, in the last article, the plasma's DNA of healthy individual and patients suffering from cancer is compared. Conclusions: The current research activities attest to the continuous convergence of Bioinformatics and Medical Informatics for clinical practice. For instance, a direct use of high throughput sequencing technologies for patients could aid the diagnosis of complex diseases (such as cancer) without invasive surgery (such as biopsy) but only with blood analysis. However, ongoing genomic tests will generate massive amounts of data and will imply new trends in the near future: “Big Data” and smart health management.

scholarly journals AlphaTims: Indexing trapped ion mobility spectrometry - time of flight data for fast and easy accession and visualization

2021 ◽  
Author(s):  
Sander Willems ◽  
Eugenia Voytik ◽  
Patricia Skowronek ◽  
Maximilian T Strauss ◽  
Matthias Mann
Keyword(s):  
Mass Spectrometry ◽  
Open Source ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Large Scale ◽  
High Resolution Mass Spectrometry ◽  
Time Of Flight ◽  
Arrival Times ◽  
Trapped Ion ◽  
Trapped Ion Mobility Spectrometry

High resolution mass spectrometry-based proteomics generates large amounts of data, even in the standard liquid chromatography (LC) - tandem mass spectrometry configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich data sets. Taking trapped ion mobility spectrometry (TIMS) on a quadrupole time-of-flight platform (Q-TOF) as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of minutes (LC), milliseconds (TIMS) and microseconds (TOF). In our open source AlphaTims package, data are indexed, accessed and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse 4D matrix and use just-in-time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events, a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in a HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happen in milliseconds. We have found AlphaTims to be a key enabling tool to explore high dimensional LC-TIMS-QTOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept ecosystem.

scholarly journals Large-scale, enzyme-based xenobiotic identification for exposomics

2020 ◽  
Author(s):  
Ken Liu ◽  
Choon Lee ◽  
Grant Singer ◽  
Michael Woodworth ◽  
Thomas Ziegler ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Retention Time ◽  
Human Disease ◽  
Large Scale ◽  
High Resolution Mass Spectrometry ◽  
Chemical Identification ◽  
Mass Spectrometry Identification ◽  
Level Of Understanding ◽  
Resolution Mass

Abstract Advances in genomics have revealed many of the genetic underpinnings of human disease, but exposomics methods are currently inadequate to obtain a similar level of understanding of environmental contributions to human disease. Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we develop and validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry (HRMS) for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time and co-occurrence with related xenobiotic metabolites. The results establish a generally applicable enzyme-based identification (EBI) for mass spectrometry identification of xenobiotic metabolites.

scholarly journals Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wei Liu ◽  
Lei Xie ◽  
Yao-Hui He ◽  
Zhi-Yong Wu ◽  
Lu-Xin Liu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Esophageal Cancer ◽  
Proteomic Analysis ◽  
Ribosomal Proteins ◽  
Large Scale ◽  
Molecular Subtypes ◽  
High Resolution Mass Spectrometry ◽  
Model Potential ◽  
Effective Strategies ◽  
Aggressive Cancer

AbstractEsophageal cancer (EC) is a type of aggressive cancer without clinically relevant molecular subtypes, hindering the development of effective strategies for treatment. To define molecular subtypes of EC, we perform mass spectrometry-based proteomic and phosphoproteomics profiling of EC tumors and adjacent non-tumor tissues, revealing a catalog of proteins and phosphosites that are dysregulated in ECs. The EC cohort is stratified into two molecular subtypes—S1 and S2—based on proteomic analysis, with the S2 subtype characterized by the upregulation of spliceosomal and ribosomal proteins, and being more aggressive. Moreover, we identify a subtype signature composed of ELOA and SCAF4, and construct a subtype diagnostic and prognostic model. Potential drugs are predicted for treating patients of S2 subtype, and three candidate drugs are validated to inhibit EC. Taken together, our proteomic analysis define molecular subtypes of EC, thus providing a potential therapeutic outlook for improving disease outcomes in patients with EC.

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