Creation of Reusable Bioinformatics Workflows for Reproducible Analysis of LC-MS Proteomics Data

Author(s):  
Julian Uszkoreit ◽  
Maike Ahrens ◽  
Katalin Barkovits ◽  
Katrin Marcus ◽  
Martin Eisenacher
2016 ◽  
Vol 15 (12) ◽  
pp. 4747-4754 ◽  
Author(s):  
Brent M. Kuenzi ◽  
Adam L. Borne ◽  
Jiannong Li ◽  
Eric B. Haura ◽  
Steven A. Eschrich ◽  
...  

2021 ◽  
Vol 47 (02) ◽  
pp. 120-128
Author(s):  
Christina Caruso ◽  
Wilbur A. Lam

AbstractHemostasis is a complex wound-healing process involving numerous mechanical and biochemical mechanisms and influenced by many factors including platelets, coagulation factors, and endothelial components. Slight alterations in these mechanisms can lead to either prothrombotic or bleeding consequences, and such hemostatic imbalances can lead to significant clinical consequences with resultant morbidity and mortality. An ideal hemostasis assay would not only address all the unique processes involved in clot formation and resolution but also take place under flow conditions to account for endothelial involvement. Global assays do exist; however, these assays are not flow based. Flow-based assays have been limited secondary to their large blood volume requirements and low throughput, limiting potential clinical applications. Microfluidic-based assays address the aforementioned limitations of both global and flow-based assays by utilizing standardized devices that require low blood volumes, offer reproducible analysis, and have functionality under a range of shear stresses and flow conditions. While still largely confined to the preclinical space, here we aim to discuss these novel technologies and potential clinical implications, particularly in comparison to the current, commercially available point-of-care assays.


2021 ◽  
Vol 22 (3) ◽  
pp. 1399
Author(s):  
Salim Ghannoum ◽  
Waldir Leoncio Netto ◽  
Damiano Fantini ◽  
Benjamin Ragan-Kelley ◽  
Amirabbas Parizadeh ◽  
...  

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ellen F. Mosleth ◽  
Christian Alexander Vedeler ◽  
Kristian Hovde Liland ◽  
Anette McLeod ◽  
Gerd Haga Bringeland ◽  
...  

AbstractDespite intensive research, the aetiology of multiple sclerosis (MS) remains unknown. Cerebrospinal fluid proteomics has the potential to reveal mechanisms of MS pathogenesis, but analyses must account for disease heterogeneity. We previously reported explorative multivariate analysis by hierarchical clustering of proteomics data of MS patients and controls, which resulted in two groups of individuals. Grouping reflected increased levels of intrathecal inflammatory response proteins and decreased levels of proteins involved in neural development in one group relative to the other group. MS patients and controls were present in both groups. Here we reanalysed these data and we also reanalysed data from an independent cohort of patients diagnosed with clinically isolated syndrome (CIS), who have symptoms of MS without evidence of dissemination in space and/or time. Some, but not all, CIS patients had intrathecal inflammation. The analyses reported here identified a common protein signature of MS/CIS that was not linked to elevated intrathecal inflammation. The signature included low levels of complement proteins, semaphorin-7A, reelin, neural cell adhesion molecules, inter-alpha-trypsin inhibitor heavy chain H2, transforming growth factor beta 1, follistatin-related protein 1, malate dehydrogenase 1 cytoplasmic, plasma retinol-binding protein, biotinidase, and transferrin, all known to play roles in neural development. Low levels of these proteins suggest that MS/CIS patients suffer from abnormally low oxidative capacity that results in disrupted neural development from an early stage of the disease.


PROTEOMICS ◽  
2009 ◽  
Vol 9 (10) ◽  
pp. 2883-2887
Author(s):  
Savita Venkataramani ◽  
Dayanand N. Naik

Data in Brief ◽  
2021 ◽  
pp. 107121
Author(s):  
Yaping Ma ◽  
Chaofan Li ◽  
Yan He ◽  
Tiwei Fu ◽  
Li Song ◽  
...  

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 336
Author(s):  
Boštjan Murovec ◽  
Leon Deutsch ◽  
Blaž Stres

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.


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