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118 results for “bioinformatic analysis”
Dataset for genes linked to gastroschisis along with bioinformatics analysis
<p>The dataset consists of records from genes linked to gastroschisis. Genes displaying statistical significance with gastroschisis (excluding those genes undergoing adjusted calculations with covariates) were selected and manually curated for further bioinformatics analysis.</p> <p>Figure 1 illustrates the systematic review of the literature search strategy and selection criteria, publishing crude (unadjusted) genes linked to gastroschisis from January 1, 1990 to August 2, 2020. </p> <p>The full list of tables is described in the file READ ME and remains available in CSV files.</p>
Figure 7. T in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 7. T. cruzi growth assessment after cisplatin treatment (300 ΜM). (a) Wild type (WT). (b) TcXPC superexpressor (Tc-TcXPC). (c) TevXPC expressor (Tc-TevXPC). The solid lines represent the untreated cells, while the dotted lines represent the cells treated with cisplatin. Statistical student's t test: (*) On that point, cells treated with cisplatin presented a statistically significant lower growth in relation to untreated cells (p <0.05). Representative results of three independent experiments.
Figure 6 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 6. Growth assessment of T. cruzi: wild type (WT), TcXPC superexpressor (Tc-TcXPC) and TevXPC expressor (Tc-TevXPC). Statistical student's t test: (*) On that point, only Tc-TevXPC presented a statistically significant lower growth in relation to WT (p <0.05); (**) On that point, both Tc-TcXPC and Tc-TevXPC presented a significant lower growth in relation to WT (p <0.05). All parasites were at same initial concentration, grown on LIT medium and were counted daily. Representative results of three independent experiments.
Figure 5 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 5. TevXPC amplification by RT-PCR with the cDNA from cell cultures. Lanes: (1) 1Kb DNA Ladder; (2) WT; (3) Tc-TcXPC; (4 and 5) Tc-TevXPC; (6) positive control (DNA from T. evansi); (7) negative control.
Figure 4 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 4. (a) TcXPB-R protein model. (b) TevXPB-R protein model. (c) TcXPB-R (blue) and TevXPB-R (orange) models overlay.
Figure 2 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 2. (a) Alignment between TcXPC and TevXPC proteins (mismatches highlighted) and its domains. Green: RAD4/PNGase transglutaminase-like fold. Blue: RAD4 beta-hairpin domain 1. Red: RAD4 beta-hairpin domain 2. Yellow: RAD4 beta-hairpin domain 3. (b) Candidate sequence motif involved in p62 interaction (highlighted by brown rectangle) found in TcXPC and TevXPC. This sequence is suggested based on the sequence motif described for Human XPC and yeast RAD4: D/E-F/W-E-D/E-V.
Figure 1 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 1. (a) TcXPC protein model. (b) TevXPC protein model. (c) TbXPC protein model (d) Model of TevXPC protein bound to a mismatch DNA. (e) TcXPC (red), TevXPC (blue) and TbXPC (green) models overlay. (f) Crystal structure of Rad4-Rad23 bound to a mismatched DNA performed by Min and Pavletich (2007).
Figure 3 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 3. (a) TcXPB protein model. (b) TevXPB protein model. (c) TcXPB (green) and TevXPB (red) models overlay.
Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma
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Pre-Analysis Bioinformatics Files for The Microbiome and Volatile Organic Compounds Reflect the State of Decomposition in an Indoor Environment
<p>Data statistics before and after trimming, FastQC reports before and after trimming, MultiQC reports before and after trimming, commands for the Kraken2-Bracken analysis, and classification reports. Read the read.me file for file names and descriptions. </p>
Bioinformática para el análisis de SARS-CoV-2 para principiantes / Bioinformatics for SARS-CoV-2 analysis for beginners
<ul> <li>Data for the e-learning tutorial <a href="https://github.com/cabana-online/Course_SARS_CoV-2">Bioinformatics for SARS-CoV-2 analysis for beginners</a></li> <li>Datos para el tutorial e-learning <a href="Bioinformática para el análisis de SARS-CoV-2 para principiantes" target="_blank" rel="noopener">Bioinformática para el análisis de SARS-CoV-2 para principiantes</a></li> </ul>
Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis"
<p>Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis".</p>
Extended Data Fig. 2-27 Geographical information of bioinformatic predicted samples based on the analysis of 16S rRNA gene in four PE degrading bacteria
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Bioinformatics Analysis from Shaw Lab for Neault et al. Cell Reports. 2023
<p>Shaw lab data associated with <span><span>PMID: </span><strong>37716355. </strong></span></p> <p><span><strong><a href="https://pubmed.ncbi.nlm.nih.gov/?sort=date&term=Neault+M&cauthor_id=37716355">Mathieu Neault</a> et al. Cell Reports. 2023</strong></span></p> <p><span><strong>Files were copied from the NetworkBiology Server</strong></span></p>
Data from: Identification of genetic defects in 33 probands with Stargardt disease by WES-based bioinformatics gene panel analysis
Stargardt disease (STGD) is the most common hereditary macular degeneration in juveniles, with loss of central vision occurring in the first or second decade of life. The aim of this study is to identify the genetic defects in 33 probands with Stargardt disease. Clinical data and genomic DNA were collected from 33 probands from unrelated families with STGD. Variants in coding genes were initially screened by whole exome sequencing. Candidate variants were selected from all known genes associated with hereditary retinal dystrophy and then confirmed by Sanger sequencing. Putative pathogenic variants were further validated in available family members and controls. Potential pathogenic mutations were identified in 19 of the 33 probands (57.6%). These mutations were all present in ABCA4, but not in the other four STGD-associated genes or in genes responsible for other retinal dystrophies. Of the 19 probands, ABCA4 mutations were homozygous in one proband and compound heterozygous in 18 probands, involving 28 variants (13 novel and 15 known). Analysis of normal controls and available family members in 12 of the 19 families further support the pathogenicity of these variants. Clinical manifestation of all probands met the diagnostic criteria of STGD. This study provides an overview of a genetic basis for STGD in Chinese patients. Mutations in ABCA4 are the most common cause of STGD in this cohort. Genetic defects in approximately 42.4% of STGD patients await identification in future studies.
Data from: Distribution and bioinformatic analysis of the cerato-platanin protein family in Dikarya
The cerato-platanin family is a group of small cysteine-rich fungal proteins new to science. They usually are abundantly secreted extracellularly and are involved in fungus-host interactions. With the advance of available fungal genome sequences, we performed a genomewide study of the distribution of this family in fungi and analyzed the common characteristics of the protein sequences. A total of 55 fungal genomes, including 27 from Ascomycota and 28 from Basidiomycota, were used. A total of 130 cerato-platanin homolog protein sequences were obtained and analyzed. Our results showed that cerato-platanin homologs existed in both Ascomycota and Basidiomycota but were lost in early branches of jelly fungi as well as in some groups with yeast or yeast-like forms in their life cycle. Homolog numbers varied considerably between Ascomycota and Basidiomycota. Phylogenetic analysis suggested that the ancestor of the Dikarya possessed multiple copies of cerato-platanins, which sorted differently in Ascomycota and Basidiomycota, and that this gene family might have expanded in the Basidiomycota. Almost all homologs contained signal peptide sequences, and the length of mature proteins were mainly 105-134 amino acids. Four cysteines involved in forming two disulfide bridges and signature sequences (CSD or CSN) were highly conserved in most homologs. These results indicated a higher diversity of the cerato-platanin family in Basidiomycota than Ascomycota.
raw data - Bioinformatics analysis reveals the m5C-associated gene DLGAP5 as a prognostic and immunological marker in bladder cancer
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Introduction to data analysis in bioinformatics
<p>A data set for the CENTURI session "Introduction to data analysis in bioinformatics"</p>
Data to reproduce bioinformatics analysis of "Morphoregulatory ADD3 underlies glioblastoma growth and formation of tumor-tumor connections"
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Data from: Identification of genetic defects in 33 probands with Stargardt disease by WES-based bioinformatics gene panel analysis
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.