br Experimental design materials and

Experimental design, materials and methods
Conflicts of interest
Acknowledgments This work was supported by grants from Proyecto del Ministerio de Economía y Competitividad (SAF2013-47556-R, co-financed with FEDER funds), and the Fondo de Investigaciones Sanitarias (ISCIII-RETIC RD12/0018/0009-FEDER). An institutional grant from the Fundación Ramón Areces is also acknowledged.
Data The presented dataset contains 10 “shotgun” human gut metagenomes assessed from stool samples from the patients with Helicobacter pylori infection. The total read length for the dataset is 87.6Gbp (the metagenomes contain 34.1±13.6mln of reads per sample, mean±s.d.). Details about the dataset are shown in Table 1.
Experimental design, materials and methods
Acknowledgments This work was financially supported by Russian Scientific Foundation (project ID 15-14-00066).
Data The data represent 99 “shotgun” metagenomes of stool samples collected from the patients with ADS and ALC in 3 clinical centers from 3 Russian cities - Moscow, Kazan and Saint-Petersburg. The datasets include 25.8±16.1mln of 50bp reads per sample (mean±s.d., 127.5Gbp in total). The description of the data is listed in Table 1.
Experimental design, materials and methods
Acknowledgments This work was financially supported by Ministry of Education and Science of the Russian Federation (unique project identifier RFMEFI60414X0119).
Data
Experimental design, materials and methods
Acknowledgements To patients that voluntarily collaborated in this patupilone study and Eunice Matos for collaborating in sample biobanking. Project partially supported by Harvard Medical School-Portugal Program (HMSP-ICJ/0022/2011), ToxOmics - Centre for Toxicogenomics and Human Health (FCT-UID/BIM/00009/2013), FCT/Poly-Annual Funding Program and FEDER/Saúde XXI Program (Portugal) and postdoctoral fellowship SFRH/BPD/43365/2008 of Fundação para a Ciência e a Tecnologia (FCT), Portugal.
Experimental design, materials and methods We designed and implemented a comprehensive, standardized, and scalable RNA-sequencing bioinformatics analysis pipeline as a workflow on the Galaxy platform [2] (http://galaxy.hunter.cuny.edu:8080/u/bioitcore/w/ted-transcriptome-data-analysis) to analyze prostate cancer RNA-sequencing datasets from the Array Express archive of the European Bioinformatics Institute (EBI) (http://www.ebi.ac.uk/arrayexpress/ experiments/E-MTAB-567/). As described in the primary study by Ren et al., the samples comprised poly-A containing RNA sequencing paired-end reads and replicates from fourteen prostate cancer patients [3]. The poly(A) random primed containing RNA were sequenced using Illumina HiSeq 2000 at a read length of 200-250nt producing on average 400 million reads for each library. The workflow requires eight input read files, one file of the human reference genome (UCSC hg19), as well as one file of the gene annotations of the reference genome. The workflow in total performs forty-four steps, using thirteen bioinformatics tools and requires approximately 84h on a 4 core processor server, with four stages:
Acknowledgments Work in Dr. Ogunwobi׳s laboratory is supported by the NIMHD/NIH grant to Hunter College: 8 G12 MD007599.
describes genotypic frequencies regarding several ANRIL SNPs in an hemodialysis cohort of patients together with the corresponding Hardy-Weinberg p-values. summarizes the main statistical parameters in the linkage disequilibrium analysis and illustrates the Haploview linkage disequilibrium plot. 2. Experimental design, materials and methods
Acknowledgements Nuria Lloberas is a researcher from ISCIII Miguel Servet (CP06/00067) and REDinRENRD12/0021/003. Ariadna Arbiol enjoys a post residence grant from Fundación José Luis Castaño. This study was supported by grants from Instituto de Salud Carlos III and Ministerio de Sanidad y Consumo (PI12/01564 and PI15/00871), and Fondo Europeo de Desarrollo Regional (FEDER).