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The Evolution of Anticancer Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Finally, signature-based approaches rely on gene signatures derived from disease-based omics data from patients before and after treatment to discover unexplored off-target or unidentified disease mechanisms. As microarray and next-generation sequencing techniques advance, vast amounts of genomics data relevant to Drug Repurposing are being accumulated which can be used to explore unknown disease-altering pathways. Examples of databases available for obtaining genomics data include NCBI-GEO, SRA (Sequence Read Archive), the CMAP (Connectivity Map), and CCLE (Cancer Cell Line Encyclopedia). As the efficacy and toxicity profile of a drug is usually associated with unique gene signatures in individuals, a gene signature database is helpful for Drug Repurposing through computational methods.
RNA-seq Analysis
Published in Altuna Akalin, Computational Genomics with R, 2020
Here we will assume that there is an RNA-seq count table comprising raw counts, meaning the number of reads counted for each gene has not been exposed to any kind of normalization and consists of integers. The rows of the count table correspond to the genes and the columns represent different samples. Here we will use a subset of the RNA-seq count table from a colorectal cancer study. We have filtered the original count table for only protein-coding genes (to improve the speed of calculation) and also selected only five metastasized colorectal cancer samples along with five normal colon samples. There is an additional column width that contains the length of the corresponding gene in the unit of base pairs. The length of the genes are important to compute RPKM and TPM values. The original count tables can be found from the recount2 database (https://jhubiostatistics.shinyapps.io/recount/) using the SRA project code SRP029880, and the experimental setup along with other accessory information can be found from the NCBI Trace archive using the SRA project code SRP029880‘1.
Introduction
Published in James Alan Duke, Rodolfo Vasquez Martinez, Amazonian Ethnobotanical Dictionary, 2018
James Alan Duke, Rodolfo Vasquez Martinez
Ah, serendipity! Sra. Maria Wright had sent me a xerox copy of Rodolfo Vasquez Martinez’ Spanish draft, Plantas Utiles de la Amazonia Peruana. I had it copied and bound to take to Amazonian Peru, without having even studied it. When Segundo started talking about a plant, I had merely to look up the local name in the index. That led me to the scientific name (key to most published ethnobotanical and phytochemical data). In later travels in Peru, I had the privilege of learning also from Antonio Montero and Lucio Pano. Their Peruvian folklore naturally matched that of Rodolfo and Segundo, confirming the value of the lore presented herein.
The interplay of gut microbiota between donors and recipients determines the efficacy of fecal microbiota transplantation
Published in Gut Microbes, 2022
Ruiqiao He, Pan Li, Jinfeng Wang, Bota Cui, Faming Zhang, Fangqing Zhao
Sequence data were downloaded from the SRA database using the accessions listed in Supplementary Table S2. When paired-end reads were available, they were merged using FLASH with default parameters.64 Only merged reads were used in downstream analyses. After that, the software AfterQC was used to perform quality control with default parameters.65 To ensure consistency with previous studies, operational taxonomic units (OTUs) were selected against the 13–8 Green Genes release and clustered at a 97% identity using QIIME pick_otu_closed_ref with the following parameters: -strand both, -id 0.97.66 Samples with fewer than 2500 OTU counts were removed. We then collapsed OTUs to the genus level with QIIME summarize and adopted standard library size normalization in log-space. All statistical analyses were performed on these normalized genus-level relative abundance data.
Improved gut microbiome recovery following drug therapy is linked to abundance and replication of probiotic strains
Published in Gut Microbes, 2022
Jamie FitzGerald, Shriram Patel, Julia Eckenberger, Eric Guillemard, Patrick Veiga, Florent Schäfer, Jens Walter, Marcus J Claesson, Muriel Derrien
As a validation of our approach, we also estimated the replication rate of the three S. thermophilus product strains in metagenomes from a cohort of 40 healthy subjects, who were not exposed to Hp treatment or antibiotics and who consumed control or the test product for a period of 1 month.25 Briefly, raw fastq files were downloaded using the fastq-dump script in SRA Toolkit and were processed and analyzed as described above. We processed 103 valid SRA runs associated with the NCBI Bio project No PRJEB35769 that consisted of an average of 43.2 ± 8.65 million quality filtered reads. These metagenomes were further analyzed for detection of S. thermophilus strain-specific clusters as described above. To evaluate the difference in product strain detection and rates of strain replication between intervention groups, scaled unique mapped reads abundance and SMEG values were compared between Test and Control using a Wilcoxon rank-sum test at timepoints D14, D28, and D42, with correction for multiple testing using the Benjamini–Hochberg method.
Gut microbiota derived metabolites contribute to intestinal barrier maturation at the suckling-to-weaning transition
Published in Gut Microbes, 2020
Martin Beaumont, Charlotte Paës, Eloïse Mussard, Christelle Knudsen, Laurent Cauquil, Patrick Aymard, Céline Barilly, Béatrice Gabinaud, Olivier Zemb, Sandra Fourre, Roselyne Gautier, Corinne Lencina, Hélène Eutamène, Vassilia Theodorou, Cécile Canlet, Sylvie Combes
Cecal content DNA was extracted using Quick-DNA Fecal/Soil Microbe 96 Kit (ZymoResearch, Irvine, CA) and the 16S rRNA V3-V4 region was amplified by PCR and sequenced by MiSeq Illumina Sequencing as previously described.55 Sequencing reads were deposited in the National Center for Biotechnology Information Sequence Read Archive (SRA accession: PRJNA572565). 16S rDNA amplicon sequences were analyzed using the FROGS pipeline according to standard operating procedures.56 Amplicons were filtered according to their size (350–500 nucleotides) and clustered into OTUs using Swarm (aggregation distance:d=1 + d=3). After chimera removal, OTUs were kept when present in at least 3 samples or representing more than 0.005% of the total number of sequences. OTUs affiliation was performed using the reference database silva132 16 S with a minimum pintail quality of 80.57 The mean number of reads per sample was 18 652 (min: 13 642 – max: 28 612). The functional potential of the microbiota was predicted by using PICRUSt258 according to the guidelines with the unrarefied OTU abundance table as input. Relative predicted abundance of MetaCyc pathways were calculated by dividing the abundance of each pathway by the sum of all pathway abundances per sample.