cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5524/100224
title: Generating music with resting-state fMRI data

description: Resting-state fMRI (rsfMRI) data generates time courses with unpredictable hills and valleys. People with musical training may notice that, to some degree, it resemble the notes of a musical scale. <br>Taking advantage of these similarities, and using only rsfMRI data as input, we use basic rules of music theory to transform the data into musical form. Our project is implemented in Python using the midiutil library. <br>We used open rsfMRI from the ABIDE dataset preprocessed by the Preprocessed Connectomes Project. We randomly chose 10 individual datasets preprocessed using C-PAC pipeline with 4 different strategies. To reduce the data dimensionality, we used the CC200 atlas to downsample voxels to 200 regions-of-interest. <br>A framework for generating music from fMRI data, based on music theory, was developed and implemented as a Python tool yielding several audio files. When listening to the results, we noticed that music differed across individual datasets. However, music generated by the same individual (4 preprocessing strategies) remained similar. Our results sound different from music obtained in a similar study using EEG and fMRI data.
DOI: 10.5524/100225
title: Highly comparable time-series analysis in Nitime

description: The aim of this project was to demonstrate that an existing Matlab-based package for implementing thousands of time-series analysis methods, hctsa could be extended to a python-based implementation, for potential future inclusion into Nitime.<br>Recent work has contributed a comprehensive library of over 35,000 pieces of diverse time-series data, and over 7,000 unique structural features extracted from hundreds of different time-series analysis methods which can be explored through an associated website and implemented using the Matlab-based code package, hctsa.<br>The hctsa software provides a systematic, algorithmic platform for computing a wide range of structural properties from a single time series, including basic statistics of the distribution, linear correlation structure, stationarity, information theoretic and entropy measures, methods from the physical nonlinear time-series analysis literature, linear and nonlinear model fits, and others.<br>Thus, hctsa can be used to map a time series to a comprehensive vector of interpretable structural features and these features can then be systematically compared to determine and understand the most useful features for a given scientific objective (e.g., features of an EEG signal that help classify different patient groups).<br>In order to apply highly comparative time-series analysis in the neuroscience community, it would be desirable to implement some time-series analysis methods into Nitime, a python-based software package for performing time-series analysis on neuroscience data.<br>Implementation of useful time-series features into python, and potential integration with Nitime, would not only facilitate their use by the neuroscience community, but also their maintenance and development within an open source framework.<br>Although there are no plans to reimplement the full hctsa feature library in python, our hope is that published work describing useful time-series features (discovered using the hctsa library) can also contribute a python implementation, to promote its use by the neuroscience community.
DOI: 10.5524/100247
title: Supporting data for "Clusterflock: A Flocking Algorithm for Isolating Congruent Phylogenomic Datasets"

description: Collective animal behavior such as the flocking of birds or the shoaling of fish has inspired a class of algorithms designed to optimize distance-based clusters in various applications including document analysis and DNA microarrays. In the flocking model, individual agents respond only to their immediate environment and move according to a few simple rules. After several iterations the agents self-organize and clusters emerge without the need for partitional seeds. In addition to their unsupervised nature, flocking offers several computational advantages including the potential to decrease the number of required comparisons. 
<br>
In Clusterflock, we implement a flocking algorithm designed to find groups (flocks) of orthologous gene families (OGFs) that share a common evolutionary history. Pairwise distances that measure the phylogenetic incongruence between OGFs guide flock formation. We test this approach on several simulated datasets varying the number of underlying topologies, the proportion of missing data, and evolutionary rates, and show that in datasets containing high levels of missing data and rate heterogeneity, Clusterflock outperforms other well-established clustering techniques. We also demonstrate its utility on a known, large-scale recombination event in Staphylococcus aureus. By isolating sets of OGFs with divergent phylogenetic signal, we can pinpoint the recombined region without forcing a pre-determined number of groupings or defining a pre-determined incongruence threshold. 
<br>
Clusterflock is an open source tool that can be used to discover horizontally transferred genes, recombined areas of chromosomes, and the phylogenetic core of a genome. Though we use it in an evolutionary context, it is generalizable to any clustering problem. Users can write extensions to calculate any distance metric on the unit interval and use these distances to flock any type of data.
DOI: 10.5524/100250
title: Supporting data for "Plant Specimen Contextual Data Consensus"

description: The Compliance and Interoperability Working Group of the Genomic Standards Consortium facilitates expert-community building and development of recommendations for description of genomic data and associated information. We present here our ongoing conation to harmonise reporting of contextual data of plant specimens associated with genomic and functional genomic data. This commentary summarises the current state of the plant sample contextual data harmonisation thus enabling engagement of a broad plant science community.<br>Here we present the Plant Specimen Contextual Data Consensus, Version 1.0. However, the consensus is likely to evolve and we therefore encourage readers to always view the latest version of the consensus at the GSC website.
DOI: 10.5524/100254
title: Draft genome of European bison (wisent), Bison bonasus

description: The wisent, also known as European bison, was rescued approximately 80 years ago from 12 founding individuals. Here, we present the draft genome sequence of a male wisent individual descended from this founding stock.
A total of 366 billion base pairs (Gb) of raw reads from whole-genome sequencing of this wisent were generated using the Illumina HiSeq2000 platform. The final genome assembly (2.58 Gb), is composed of 29,074 scaffolds with an N50 of 4.7 Mb. 47.3% of
the genome is composed of repetitive elements. We identified 21,542 genes and
58,385 non-coding RNAs. A phylogenetic tree based on nuclear genomes indicated
sister relationships between bison and wisent and between the wisent-bison clade and
yak. For 75 genes we obtained evidence of positive evolution in the wisent lineage.
We provide the first genome sequence and gene annotation for the wisent. The availability of these resources will be of value for the future conservation of this endangered large mammal and for reconstructing the evolutionary history of the Bovini
tribe.
DOI: 10.5524/100172
title: Supporting data for "De novo construction of an expanded transcriptome assembly for the western tarnished plant bug, Lygus hesperus".

description: The plant bug, <em>Lygus hesperus</em> Knight, is a polyphagous pest of many economically important crops. Despite its pest status, little is known about the molecular mechanisms responsible for much of Lygus biology. Earlier Lygus transcriptome assemblies were either limited by low read depth or focused on specific conditions. To generate a more comprehensive transcriptome, we supplemented previous datasets with new reads corresponding to specific tissues (heads, antennae, and male reproductive tissues). This transcriptome augments current Lygus molecular resources and provides the foundational knowledge critical for future comparative studies.
<br>
An expanded, Trinity-based de novo transcriptome assembly for L. hesperus was generated using previously published whole body Illumina data supplemented with 293 million bp of new raw sequencing data corresponding to five tissue-specific cDNA libraries and 11 Illumina sequencing runs. The updated transcriptome consists of 22,022 transcripts (average length of 2,075 nt), 62% of which contain complete open reading frames. Significant coverage of the BUSCO (benchmarking universal single-copy orthologs) dataset and robust metrics indicate that the transcriptome is a quality assembly with a high degree of completeness. Initial assessment of assembly utility revealed that the length and abundance of transcripts predicted to regulate insect physiology and chemosensation have improved with the new assembly compared to previous <em>L. hesperus</em> assemblies.
DOI: 10.5524/100174
title: Genomic assembled sequences and annotation for Phytophthora nicotianae race 0 and race 1

description: Black shank is a severe plant disease caused by soil-born pathogen <em>Phytophthora nicotianae</em>. Two physiological races of <em>P. nicotianae</em>, race 0 and race 1, were predominantly observed in the cultivated tobacco field around the world. Race 0 was reported to be more aggressive, having shorter incubation period and causing worse root rot symptoms, while race 1 causing greater necrosis. So far, the molecular mechanisms underlying the virulence difference between race 0 and 1 remain elusive. <br>
We assembled and annotated genomes of <em>P. nicotianae</em> race 0 and 1, which were obtained by a combination of PacBio single molecular real time sequencing and second-generation sequencing (both HiSeq and MiSeq platforms). Gene family analysis found an ATP-binding cassette transporter gene family under expansion in <em>P. nicotianae</em> Particularly, more RxLR effector genes were found in the genome of race 0 than that of race 1. Besides, RxLR effector genes are mainly distributed in gene-sparse, repeat-rich regions of the <em>P. nicotianae</em> genome.<br> The final assembled genome sizes for race 0 and race 1 were 80 Mb and 69 Mb, which is slightly different from the previous estimation of <em>P. nicotianae</em> (90 Mb). The corresponding contig N50 sizes were 23 Kb and 30 Kb, respectively A total of 17,797 and 14,542 protein-coding genes were annotated in P. nicotianae race 0 and race 1, respectively.
DOI: 10.5524/100182
title: Supporting single-molecule optical genome mapping data from human HapMap and colorectal cancer cell lines.

description: Next generation sequencing (NGS) technologies have changed our understanding of the variability of the human genome. However, the identification of genome structural variations based on NGS approaches with read lengths of 35 to 300 bases remains to be a challenge. Single molecule optical mapping technologies allow the analysis of DNA molecules of up to 2 Mb and are very suitable for the identification of large scale genome structural variations and for de novo genome assemblies when combined with short read NGS data. Here we present the optical mapping data of two human genomes: the HapMap cell line GM12878 and the colorectal cancer cell line HCT116. <br>
High molecular weight DNA was obtained by embedding GM12878 and HCT116 cells, respectively, in agarose plugs followed by DNA extraction under mild conditions. We digested genomic DNA with KpnI and analyzed 310,000 and 296,000 DNA molecules ( 150 kb and 10 restriction fragments), respectively, per cell line using the Argus optical mapping system. We aligned the maps to the human reference by OPTIMA, a new glocal alignment method, and obtained 6.8x and 5.7x genome coverage, 2.9x and 1.7x more than the coverage obtained with previously available software.
DOI: 10.5524/100204
title: Supporting data for "Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets".

description: Bioinformatics software often requires human-generated tabular text files as input and have specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians, and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support concurrent editing of a single spreadsheet by different users working on different platforms. Often most of the researchers who are entering data will not be familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis.
<br>
We present Keemei, a Google Sheets Add-on for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Googles Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports validation of two widely used tabular bioinformatics formats, the QIIME sample metadata mapping file format, and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others. <br> Included in this GigaDB dataset are the archival code and test data as reviewed at the time of publication, for the most recent version of the application please visit the Keemei website.
DOI: 10.5524/100205
title: Supporting data for "Tools and techniques for computational reproducibility".

description: When reporting research findings, scientists document the steps they followed so that others can verify and build upon the research. When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible. Computers play a vital role in many research disciplines and present both opportunities and challenges for reproducibility. Computers can be programmed to execute analysis tasks, and those programs can be repeated and shared with others. Due to the deterministic nature of most computer programs, the same analysis tasks, applied to the same data, will often produce the same outputs. However, in practice, computational findings often cannot be reproduced due to complexities in how software is packaged, installed, and executedand due to limitations in how scientists document analysis steps. Many tools and techniques are available to help overcome these challenges. Here we describe seven such strategies. With a broad scientific audience in mind, we describe strengths and limitations of each approach, as well as circumstances under which each might be applied. No single strategy is sufficient for every scenario; thus we emphasize that it is often useful to combine approaches.<br>The files presented in this GigaDB dataset are those used to generate the figures and examples used in the GigaScience review article, and they demonstrate how reproducibility can be achieved using some of the tools discussed in the manuscript.
DOI: 10.5524/100214
title: Genome of the ocean sunfish (Mola mola).

description: The ocean sunfish (Mola mola) which can grow up to a length of 2.7 m and weigh 2.3 tons is the worlds largest bony fish. It exhibits an extremely fast growth rate and possesses an endoskeleton made up largely of cartilage. Another unique feature of the sunfish is the loss of caudal fin which is replaced by a broad and stiff lobe resulting in the characteristic truncated appearance of the fish.
To get an insight into the genomic bases of these derived phenotypes, we sequenced the ocean sunfish genome and performed comparative analysis with other teleost genomes. Several ocean sunfish genes involved in the GH/IGF1 axis signaling pathway were found to be under positive selection or accelerated evolution, which might explain its fast growth rate and large body size. A number of genes associated with the extracellular matrix, some of which are involved in the regulation of bone and cartilage development, have also undergone positive selection or accelerated evolution. The ocean sunfish contains seven Hox clusters similar to fugu but contains more Hox genes than fugu. Thus, it appears that the caudal fin loss in ocean sunfish is not associated with the loss of any specific Hox gene. 
Our analyses provide insights into the molecular basis of the fast growth rate and large size of the sunfish. The high-quality genome assembly generated in this study should facilitate further studies of this natural mutant.
DOI: 10.5524/100233
title: Sharing data in the cloud

description: Cloud computing resources, such as Amazon Web Services (AWS), provide pay-as-you-go access to high-performance computer resources and dependable data storage solutions for performing large scale analyses of neuroimaging data . These are particularly attractive for researchers at small universities and in developing countries who lack the wherewithal to maintain their own high performance computing systems. The objective of this project is to upload data from the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Datasharing Initiatives (INDI) grass-roots data sharing initiatives into a Public S3 Bucket that has been generously provided by AWS. <br>The entirety of the CoRR, ABIDE, ACPI, and ADHD-200 data collections and ENKIRS data for 427 individuals were uploaded during the OHBM Hackathon event. The data are available as individual files to make it easily index able by database infrastructures such as COINs LORIS and others. Additionally, this makes it easy for the users to download just the data that they want. The data in the bucket can be browsed and downloaded using a GUI based S3 file transfer software such as Cyberduck, or using the Boto python library. <br>The data is structured as follows: bucketname/data/Projects/ProjectName/DataType. For example you can access raw data from the ENKI-RS, by specifying the following path in CyberDuck: https://s3.amazon.com/fcp-indi/data/Projects/RocklandSample/RawData.<br>Uploading data shared through the FCP and INDI initiatives improves its accessibility for cloud-based and local computation. Future efforts for this project will include uploading the remainder of the FCP and INDI data and organizing the data in the new brain imaging data structure (BIDS) format.
DOI: 10.5524/100154
title: Supporting data for "Addressing health disparities in Hispanic breast cancer: Accurate and inexpensive sequencing of BRCA1 and BRCA2".

description: Germline mutations in the BRCA1 and BRCA2 genes account for 20 to 25% of inherited breast cancer and about 10% of all breast cancer cases. Detection of BRCA mutation carriers can lead to therapeutic interventions such as mastectomy, oophorectomy, hormonal prevention therapy, improved screening, and targeted therapies such as PARP-inhibition. We estimate that African Americans and Hispanics are 4 to 5 times less likely to receive BRCA screening, despite having similar mutation frequencies as non-Jewish Caucasians and these populations have higher breast cancer mortality. To begin addressing this health disparity, we initiated a nationwide trial of BRCA testing of Latinas with breast cancer. Patients were recruited through community organizations, clinics, public events and through the mail and internet. Subjects completed the consent process and questionnaire, and provided a saliva sample by mail or in person. DNA from 120 subjects was used to sequence the entire BRCA1 and BRCA2 coding regions and splice sites, and validation of pathogenic mutations with a total material cost of $85/subject. Subjects ranged in age from 23-81 years (mean age of 51 years), 6% had bilateral disease, 57% were ER/PR+, 23% HER2+, and 17% had triple-negative disease.
DOI: 10.5524/100169
title: Supporting material for "High throughput identification of novel conotoxins from the Chinese tubular cone snail (Conus betulinus) by multi-transcriptome sequencing".

description: The venom of predatory marine cone snails mainly contains a diverse array of unique bioactive peptides commonly referred to as conopeptides or conotoxins. These peptides have proven to be valuable pharmacological probes and potential drugs because of their high specificity and affinity to important ion channels, receptors and transporters of the nervous system. Most of previous studies have specifically focused on the conopeptides from piscivorous and molluscivorous cone snails, but little attention has been devoted to the dominant vermivorous species.
The vermivorous Chinese tubular cone snail, Conus betulinus, is the dominant Conus species inhabiting in the South China Sea. The transcriptomes of venom ducts and venom bulbs from six specimens of this species were sequenced using both next-generation sequencing and traditional Sanger sequencing technologies, resulting in the identification of a total of 215 distinct conopetides. Among these, 183 were novel conopeptides, including 9 new superfamilies. It appears that most of the identified conopeptides were synthesized in the venom duct, while a handful of conopeptides were identified only in the venom bulb at very low levels.
A variance in conopeptides from different specimens of C. betulinus was observed, which suggested presence of intraspecific variability in toxicity at the genetic level. 
The raw sequence data are available from the Sequence Read Archives, and here we provide the BLAST resutls and our annotations of this potentially fertile resource for the development of new pharmaceutics.
DOI: 10.5524/100171
title: Software and exemplar data for Rcorrector.

description: Next generation sequencing of cellular RNA (RNA-seq) is rapidly becoming the cornerstone of transcriptomic analysis. However, sequencing errors in the already short RNA-seq reads complicate bioinformatics analyses, in particular alignment and assembly. Error correction methods have been highly effective for whole genome sequencing (WGS) reads, but are unsuitable for RNA-seq reads, due to the variation in gene expression levels and alternative splicing.
<br>We developed a k-mer based method, Rcorrector, to correct random sequencing errors in Illumina RNA-seq reads. Rcorrector uses a De Bruijn graph to compactly represent all trusted k-mers in the input reads. Unlike WGS read correctors, which employ a global threshold to determine trusted k-mers, Rcorrector computes a local threshold at every position in a read.<br> The software as published is available directly from here, but for the most up to date version please see the project GitHub https://github.com/mourisl/Rcorrector/ repository.