cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2009241
title: LP RNA

description: Laryngo-pharyngeal squamous cell carcinomas are one of the most common head and neck cancers. Despite the presence of a large body of information, molecular biomarkers are not currently used in the diagnosis, treatment and management of patients for this group of cancer. Here, we have profiled expression of genes and microRNAs of larynx and hypopharynx tumors using high-throughput sequencing experiments. We found that matrix metalloproteinases along with SCEL, CRNN, KRT4, SPINK5, and TGM3 among others have significantly altered expression in these tumors. Alongside gene expression, the microRNAs hsa-miR-139, hsa-miR-203 and the hsa-miR-424/503 cluster have aberrant expression in these cancers. Using target genes for these microRNAs, we found the involvement of pathways linked to cell cycle, p53 signaling, and viral carcinogenesis significant (P-values 10−13, 10−9 and 10−7 respectively). Finally, using an ensemble machine-learning tool, we discovered a unique 8-gene signature for this group of cancers that differentiates the group from the other tumor subsites of head and neck region. We investigated the role of promoter methylation in one of these genes, WIF1, and found no correlation between DNA methylation and down-regulation of WIF1. We validated our findings of gene expression, 8-gene signature and promoter methylation using q-PCR, data from TCGA and q-MSP respectively.<br>Data presented in this manuscript has been submitted to the NCBI Geo database with the accession number GSE67994.
DOI: 10.6084/m9.figshare.2009244
title: Exome Scripts

description: The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.
DOI: 10.6084/m9.figshare.2009247
title: Exome

description: The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.
DOI: 10.6084/m9.figshare.2009238
title: SchulzThöni2016PONE.zip

description: The two files contain the data from the publication:<br>Schulz, J., Thöni, C. 2016. Overconfidence and career choice. Plos ONE.<br><br>In text format (tab delimited)<br>- SchulzThöni2016PONE.txt<br><br><br>In Stata format (version 12)<br>- SchulzThöni2016PONE.dta
DOI: 10.6084/m9.figshare.2009241.v1
title: LP RNA

description: Laryngo-pharyngeal squamous cell carcinomas are one of the most common head and neck cancers. Despite the presence of a large body of information, molecular biomarkers are not currently used in the diagnosis, treatment and management of patients for this group of cancer. Here, we have profiled expression of genes and microRNAs of larynx and hypopharynx tumors using high-throughput sequencing experiments. We found that matrix metalloproteinases along with SCEL, CRNN, KRT4, SPINK5, and TGM3 among others have significantly altered expression in these tumors. Alongside gene expression, the microRNAs hsa-miR-139, hsa-miR-203 and the hsa-miR-424/503 cluster have aberrant expression in these cancers. Using target genes for these microRNAs, we found the involvement of pathways linked to cell cycle, p53 signaling, and viral carcinogenesis significant (P-values 10−13, 10−9 and 10−7 respectively). Finally, using an ensemble machine-learning tool, we discovered a unique 8-gene signature for this group of cancers that differentiates the group from the other tumor subsites of head and neck region. We investigated the role of promoter methylation in one of these genes, WIF1, and found no correlation between DNA methylation and down-regulation of WIF1. We validated our findings of gene expression, 8-gene signature and promoter methylation using q-PCR, data from TCGA and q-MSP respectively.<br>Data presented in this manuscript has been submitted to the NCBI Geo database with the accession number GSE67994.
DOI: 10.6084/m9.figshare.2009244.v1
title: Exome Scripts

description: The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.
DOI: 10.6084/m9.figshare.2009232
title: ChromatinAssortativity

description: Code associated to the paper: <br>Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativityVera PancaldiEnrique Carrillo-de-Santa-Pau, Biola Maria Javierre, David Juan, Peter Fraser, Mikhail Spivakov, Alfonso Valencia and Daniel Rico<br><br><br>
DOI: 10.6084/m9.figshare.2009235
title: MUSIC

description: High-throughput DNA sequencers are becoming indispensable in our understanding of diseases atmolecular level, in marker-assisted selection in agricultureand in microbial genetics research. These sequencinginstruments produce enormous amount of data (oftenterabytes of raw data in a month) that requires efficientanalysis, management and interpretation. The commonlyused sequencing instrument today produces billions ofshort reads (upto 150 bases) from each run. The first stepin the data analysis step is alignment of these short reads tothe reference genome of choice. There are different opensource algorithms available for sequence alignment to thereference genome. These tools normally have a highcomputational overhead, both in terms of number ofprocessors and memory. Here, we propose a hybrid computing environment called MUSIC (Mapping USInghybrid Computing) for one of the most popular open sourcesequence alignment algorithm, BWA, using acceleratorsthat show significant improvement in speed over the serialcode
DOI: 10.6084/m9.figshare.2009247.v1
title: Exome

description: The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.
DOI: 10.6084/m9.figshare.2009229
title: SINC Scripts

description: BackgroundThe rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.<br>ResultsWe report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.<br>ConclusionsWe have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator
DOI: 10.6084/m9.figshare.2009235.v1
title: MUSIC

description: High-throughput DNA sequencers are becoming indispensable in our understanding of diseases atmolecular level, in marker-assisted selection in agricultureand in microbial genetics research. These sequencinginstruments produce enormous amount of data (oftenterabytes of raw data in a month) that requires efficientanalysis, management and interpretation. The commonlyused sequencing instrument today produces billions ofshort reads (upto 150 bases) from each run. The first stepin the data analysis step is alignment of these short reads tothe reference genome of choice. There are different opensource algorithms available for sequence alignment to thereference genome. These tools normally have a highcomputational overhead, both in terms of number ofprocessors and memory. Here, we propose a hybrid computing environment called MUSIC (Mapping USInghybrid Computing) for one of the most popular open sourcesequence alignment algorithm, BWA, using acceleratorsthat show significant improvement in speed over the serialcode
DOI: 10.6084/m9.figshare.2009586.v8
title: NEON Spatio-temporal - Teaching Data Subset

description: This includes teaching data subsets that contains spatio-temporal data for the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range (SJER) field sites.CONTENTS:NEON-DS-Met-Time-Series-HARV.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest site. NOTE: these data are not collected from the NEON tower. NEON-DS-Site-Layout-Files-HARV.zip: A set of shapefiles for the NEON's Harvard Forest field site and US and (some) state boundary layers.NEON-DS-Landsat-NDVI-HARV-SJER.zip: 2011 NDVI data product provided by USGS cropped to NEON's Harvard Forest and San Joaquin Experimental Range field sites.NEON-DS-Airborne-RemoteSensing-HARV-SJER.zip: LiDAR data including a canopy height model, digital elevation model and digital surface model for NEON's Harvard Forest and San Joaquin Experimental Range field sites.
DOI: 10.6084/m9.figshare.2009229.v1
title: SINC Scripts

description: BackgroundThe rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.<br>ResultsWe report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.<br>ConclusionsWe have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator
DOI: 10.6084/m9.figshare.2009238.v2
title: SchulzThöni2016PONE.zip

description: The two files contain the data from the publication:<br>Schulz, J., Thöni, C. 2016. Overconfidence and career choice. Plos ONE.<br><br>In text format (tab delimited)<br>- SchulzThöni2016PONE.txt<br><br><br>In Stata format (version 12)<br>- SchulzThöni2016PONE.dta
DOI: 10.6084/m9.figshare.2009223
title: SINC

description: BackgroundThe rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.<br>ResultsWe report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.<br>ConclusionsWe have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator
DOI: 10.6084/m9.figshare.2009223.v1
title: SINC

description: BackgroundThe rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.<br>ResultsWe report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.<br>ConclusionsWe have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator
DOI: 10.6084/m9.figshare.2009238.v1
title: SchulzThöni2016PONE.zip

description: The two files contain the data from the publication:<br>Schulz, J., Thöni, C. 2016. Overconfidence and career choice. Plos ONE.<br><br>In text format (tab delimited)<br>- SchulzThöni2016PONE.txt<br><br><br>In Stata format (version 12)<br>- SchulzThöni2016PONE.dta
DOI: 10.6084/m9.figshare.2009586.v9
title: NEON Spatio-temporal - Teaching Data Subset

description: This includes teaching data subsets that contains spatio-temporal data for the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range (SJER) field sites.CONTENTS:NEON-DS-Met-Time-Series-HARV.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest site. NOTE: these data are not collected from the NEON tower. NEON-DS-Site-Layout-Files-HARV.zip: A set of shapefiles for the NEON's Harvard Forest field site and US and (some) state boundary layers.NEON-DS-Landsat-NDVI-HARV-SJER.zip: 2011 NDVI data product provided by USGS cropped to NEON's Harvard Forest and San Joaquin Experimental Range field sites.NEON-DS-Airborne-RemoteSensing-HARV-SJER.zip: LiDAR data including a canopy height model, digital elevation model and digital surface model for NEON's Harvard Forest and San Joaquin Experimental Range field sites.
DOI: 10.6084/m9.figshare.2009217
title: Proteomics_transcriptomics_LP

description: The study highlights the need to optimize tissue processing and analytical pipelines to 43 enable accurate integrated analysis of high throughput omics data; a sequential extraction of 44 RNA and protein entities and subsequent integrated analysis was identified to provide a 45 better representation of the molecular profile in terms concordant entities and pathways.