cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8311277.v1
title: Analyzing aggregation propensities of clinically relevant PTEN mutants: a new culprit in pathogenesis of cancer and other PTENopathies

description: While studies on pathological protein aggregation are largely limited to neurodegenerative disease, emerging evidence suggests that other diseases are also associated with pathogenic protein aggregation. For example, tumor suppressor protein p53, and its mutant conformers, undergo protein aggregation, exacerbating the cancer phenotype. These findings raise the possibility that inactivation of tumor suppressors via protein aggregation may participate in cancer and other disease pathologies. Since tumor suppressor protein PTEN has similar functions to p53, and is mutated in multiple diseases, we examined the aggregation propensity of PTEN wild-type and 1523 clinically relevant PTEN mutants. Applying computational tools to PTEN mutation databases revealed that PTEN wild-type protein can aggregate under physiological conditions, and 274 distinct PTEN mutants had increased aggregation propensity. To understand the mechanism underlying PTEN conformer aggregation, we analyzed the physicochemical properties of these 274 PTEN mutants and defined their aggregation potential. We conclude that increased aggregation propensity of select PTEN mutants may contribute to disease phenotypes. Our studies have built the foundation for interrogating the aggregation potential of these select mutants in cancers and in PTENopathies. Elucidating the pathogenic mechanisms associated with aggregation-prone PTEN conformers will aid in developing therapies that target PTEN-aggregates in multiple diseases. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.8311274.v1
title: Broadband Japan

description: Eye-tracking Data Broadband Face Japan
DOI: 10.6084/m9.figshare.8311268
title: Morphology DNRS.xlsx

description: Valley width, channel width, channel sinuosity, and channel bed elevation data from the Dong Nai River System in Vietnam
DOI: 10.6084/m9.figshare.8311265
title: Filtering

description: Filtering script
DOI: 10.6084/m9.figshare.8311265.v1
title: Filtering

description: Filtering script
DOI: 10.6084/m9.figshare.8311256
title: Filtered Japan

description: eye-tracking data Filtered Faces Japan
DOI: 10.6084/m9.figshare.8311268.v1
title: Morphology DNRS.xlsx

description: Valley width, channel width, channel sinuosity, and channel bed elevation data from the Dong Nai River System in Vietnam
DOI: 10.6084/m9.figshare.8311241
title: Nepartak

description: Nepartak wrfout data
DOI: 10.6084/m9.figshare.8311241.v1
title: Nepartak

description: Nepartak wrfout data
DOI: 10.6084/m9.figshare.8311268.v2
title: Morphology DNRS.xlsx

description: Valley width, channel width, channel sinuosity, and channel bed elevation data from the Dong Nai River System in Vietnam
DOI: 10.6084/m9.figshare.8311268.v4
title: Morphology DNRS.xlsx

description: Valley width, channel width, channel sinuosity, and channel bed elevation data from the Dong Nai River System in Vietnam
DOI: 10.6084/m9.figshare.8311268.v3
title: Morphology DNRS.xlsx

description: Valley width, channel width, channel sinuosity, and channel bed elevation data from the Dong Nai River System in Vietnam
DOI: 10.6084/m9.figshare.8311073
title: Composite measure

description: The code used to determine the protective index of Drosophila against the France and Madeira wasp strain in the presence and absence of ethanol
DOI: 10.6084/m9.figshare.8311067
title: Annotation report of the Trinity de novo assembled transcripts of Ajowan inflorescence

description: The dataset file include the functional annotation of the ajowan<br>transcriptome was done using a Trinotate annotation pipeline (http://trinotate.github.io/). <br>
DOI: 10.6084/m9.figshare.8311073.v2
title: Composite measure

description: The code used to determine the protective index of Drosophila against the France and Madeira wasp strain in the presence and absence of ethanol
DOI: 10.6084/m9.figshare.8311067.v2
title: Annotation report of the Trinity de novo assembled transcripts of Ajowan inflorescence

description: The dataset file include the functional annotation of the ajowan<br>transcriptome was done using a Trinotate annotation pipeline (http://trinotate.github.io/). <br>
DOI: 10.6084/m9.figshare.8311073.v1
title: Composite measure

description: The code used to determine the protective index of Drosophila against the France and Madeira wasp strain in the presence and absence of ethanol
DOI: 10.6084/m9.figshare.8311070.v1
title: Patience Experimental Results

description: Files containing experimental results for our work on Patience and Solitaire games including Klondike, Canfield and others.<br>File is a tarred and gzipped archive of many files in a number of directories, the most important being many .csv files. Each line of a csv file contains an experimental result (not necessarily conclusive) of a run of Solvitaire on a randomly generated layout of a particular game of patience.<br>
DOI: 10.6084/m9.figshare.8311067.v1
title: Trinotate annotation report of the Trinity assembled transcripts of Ajowan inflorescence

description: The dataset file include the functional annotation of the ajowan<br>transcriptome was done using a Trinotate annotation pipeline (http://trinotate.github.io/). <br>
DOI: 10.6084/m9.figshare.8311070.v2
title: Patience Experimental Results

description: Files containing experimental results for our work on Patience and Solitaire games including Klondike, Canfield and others.<br>File is a tarred and gzipped archive of many files in a number of directories, the most important being many .csv files. Each line of a csv file contains an experimental result (not necessarily conclusive) of a run of Solvitaire on a randomly generated layout of a particular game of patience.<br>
DOI: 10.25392/leicester.data.8310995
title: Frequency & Predictability Effects on Eye Movements of Chinese Developing Readers

description: This is a dataset for an experiment on the effects of word frequency and the contextual predictability of words on the eye movements of developing Chinese readers.
DOI: 10.6084/m9.figshare.8310998
title: Nepartak wrfout data

description: Nepartak wrfout data
DOI: 10.6084/m9.figshare.8310998.v2
title: Nepartak wrfout data

description: Nepartak wrfout data
DOI: 10.6084/m9.figshare.8310773
title: Data validation files of six tailing samples metagenome

description: Quality control files of the metagenomic analysis of the six tailing samples
DOI: 10.6084/m9.figshare.8310998.v1
title: wrfout_d03_2016-07-04_00_00_00

description: Nepartak wrfout data
DOI: 10.6084/m9.figshare.8310632
title: Additional file 8: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S6. Clontech subsample analysis. The original number of reads was retained for all methods other than Clontech. The variation in the number of detected miRNAs (number of those with &gt;â 10 normalized reads) for the other methods demonstrates the influence of the inclusion of the different Clontech subsets on the DESeq2 normalization. The normalization in this case only included these samples, therefore there may be differences compared to the other tables. (XLSX 34 kb)
DOI: 10.6084/m9.figshare.8310632.v1
title: Additional file 8: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S6. Clontech subsample analysis. The original number of reads was retained for all methods other than Clontech. The variation in the number of detected miRNAs (number of those with &gt;â 10 normalized reads) for the other methods demonstrates the influence of the inclusion of the different Clontech subsets on the DESeq2 normalization. The normalization in this case only included these samples, therefore there may be differences compared to the other tables. (XLSX 34 kb)
DOI: 10.6084/m9.figshare.8310626
title: Additional file 6: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S4..Statistics on percentages of reads mapping to different types of RNAs. Statistics are shown for the t-test comparisons of the percentages of reads mapping to each type of RNA for the data derived from the same human brain using 1000 ng of total RNA for each library preparation kit, using both batches. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 199% more snRNA mapped reads than Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. miRNA = microRNA, piRNA = PIWI interacting RNA, rRNA = ribosomal RNA, snoRNA = small nucleolar RNA, snRNA = small nuclear RNA, tRNA = transfer RNA. Significant findings are highlighted in yellow. (XLSX 42 kb)
DOI: 10.6084/m9.figshare.8310629
title: Additional file 7: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S5. Number of miRNA and isomiRs detected by each method. The top half of the table shows the number of miRNAs detected above 10 normalized counts for each method in all 3 triplicates, the number detected above the threshold in both batches, and the percentage of miRNAs detected in the first batch that were not detected in the second batch. The bottom half of the table shows the number of non-canonical miRNA sequences, called isomiRs, detected above 100 normalized counts for each method in all 3 triplicates, the number detected above the threshold in both batches, and the percentage of isomiRs detected in the first batch that were not detected in the second batch. (XLSX 33 kb)
DOI: 10.6084/m9.figshare.8310626.v1
title: Additional file 6: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S4..Statistics on percentages of reads mapping to different types of RNAs. Statistics are shown for the t-test comparisons of the percentages of reads mapping to each type of RNA for the data derived from the same human brain using 1000 ng of total RNA for each library preparation kit, using both batches. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 199% more snRNA mapped reads than Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. miRNA = microRNA, piRNA = PIWI interacting RNA, rRNA = ribosomal RNA, snoRNA = small nucleolar RNA, snRNA = small nuclear RNA, tRNA = transfer RNA. Significant findings are highlighted in yellow. (XLSX 42 kb)
DOI: 10.6084/m9.figshare.8310629.v1
title: Additional file 7: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S5. Number of miRNA and isomiRs detected by each method. The top half of the table shows the number of miRNAs detected above 10 normalized counts for each method in all 3 triplicates, the number detected above the threshold in both batches, and the percentage of miRNAs detected in the first batch that were not detected in the second batch. The bottom half of the table shows the number of non-canonical miRNA sequences, called isomiRs, detected above 100 normalized counts for each method in all 3 triplicates, the number detected above the threshold in both batches, and the percentage of isomiRs detected in the first batch that were not detected in the second batch. (XLSX 33 kb)
DOI: 10.6084/m9.figshare.8310620
title: Additional file 5: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S3. Percentage of reads mapping to various types of small RNA. Columns E-K show the number of reads mapping to each type of RNA, while L shows the total number of reads. Columns N-T show the percentage of reads of the total mapped reads that map to each type of small RNA. Column U shows the percentage of the total reads that did not map to any off the evaluated RNAs. Column V through AB show the percentage of reads of all reads that map to each type of small RNA. miRNA = microRNA, piRNA = PIWI interacting RNA, rRNA = ribosomal RNA, snoRNA = small nucleolar RNA, snRNA = small nuclear RNA, tRNA = transfer RNA. Synthetic samples from are highlighted in red. These samples were only aligned to the synthetic sequences. (XLSX 77 kb)
DOI: 10.6084/m9.figshare.8310620.v1
title: Additional file 5: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S3. Percentage of reads mapping to various types of small RNA. Columns E-K show the number of reads mapping to each type of RNA, while L shows the total number of reads. Columns N-T show the percentage of reads of the total mapped reads that map to each type of small RNA. Column U shows the percentage of the total reads that did not map to any off the evaluated RNAs. Column V through AB show the percentage of reads of all reads that map to each type of small RNA. miRNA = microRNA, piRNA = PIWI interacting RNA, rRNA = ribosomal RNA, snoRNA = small nucleolar RNA, snRNA = small nuclear RNA, tRNA = transfer RNA. Synthetic samples from are highlighted in red. These samples were only aligned to the synthetic sequences. (XLSX 77 kb)
DOI: 10.6084/m9.figshare.8310605
title: Additional file 2: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S2. Statistics for paired t-tests of â accuracy errorâ of each synthetic miRNA from the mean for each method. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 4.55% less error than Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. (XLSX 41 kb)
DOI: 10.6084/m9.figshare.8310599
title: Additional file 1: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S1. Table shows the top 20 most abundant miRNAs for each method. The 6 miRNAs that were commonly among the top 20 for all of the methods are highlighted in different colors. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8310605.v1
title: Additional file 2: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S2. Statistics for paired t-tests of â accuracy errorâ of each synthetic miRNA from the mean for each method. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 4.55% less error than Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. (XLSX 41 kb)
DOI: 10.6084/m9.figshare.8310596
title: Additional file 17: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S13. Evaluation of the most abundant miRNA in the NEXTflex samples. To determine if our UMI length was adequate, we estimated the number of starting sequences for the most abundant miRNA (miR-9-5p) in the NEXTflex data. For comparison sake, we also show the estimated number of starting molecules for a more moderately abundant miRNA (miR-137). The possible number of starting sequences for each miRNA was estimated assuming a PCR amplification efficiency of only 62.5%, all samples, thus enabling more conservative estimates for the number of starting sequences. PCR efficiencies of miRNA generally range from 75 to 95%, and previous studies demonstrate that the PCR efficiency of miR-9-5p has been 98%. Therefore, our estimates of the number of starting miR-9-5p sequences are likely higher than the true number of starting sequences. (XLSX 44 kb)
DOI: 10.6084/m9.figshare.8310599.v1
title: Additional file 1: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S1. Table shows the top 20 most abundant miRNAs for each method. The 6 miRNAs that were commonly among the top 20 for all of the methods are highlighted in different colors. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8310596.v1
title: Additional file 17: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S13. Evaluation of the most abundant miRNA in the NEXTflex samples. To determine if our UMI length was adequate, we estimated the number of starting sequences for the most abundant miRNA (miR-9-5p) in the NEXTflex data. For comparison sake, we also show the estimated number of starting molecules for a more moderately abundant miRNA (miR-137). The possible number of starting sequences for each miRNA was estimated assuming a PCR amplification efficiency of only 62.5%, all samples, thus enabling more conservative estimates for the number of starting sequences. PCR efficiencies of miRNA generally range from 75 to 95%, and previous studies demonstrate that the PCR efficiency of miR-9-5p has been 98%. Therefore, our estimates of the number of starting miR-9-5p sequences are likely higher than the true number of starting sequences. (XLSX 44 kb)
DOI: 10.6084/m9.figshare.8310578
title: Additional file 14: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S11. Pairwise comparisons of triplicate inconsistency of brain samples. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 29.56% more error than NEB in the comparison of triplicate consistency of the 1000â ng samples. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are shown in yellow. (XLSX 42 kb)
DOI: 10.6084/m9.figshare.8310584
title: Additional file 15: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S12. Pairwise comparisons of triplicate inconsistency error bewteen starting amounts. Statistics for t-tests of triplicate inconsistency error between different starting amounts for each tested method are shown. Percent difference is calculated as the percentage that the first amount in the comparison is relative to the second, thus Fivepercent 100 ng data had 13.84% more error than the 250 ng data. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. Illumina did not show a significant difference in consistency between the tested starting amounts, (F = 1.44, p = 0.23). (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8310578.v1
title: Additional file 14: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S11. Pairwise comparisons of triplicate inconsistency of brain samples. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 29.56% more error than NEB in the comparison of triplicate consistency of the 1000â ng samples. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are shown in yellow. (XLSX 42 kb)
DOI: 10.6084/m9.figshare.8310584.v1
title: Additional file 15: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S12. Pairwise comparisons of triplicate inconsistency error bewteen starting amounts. Statistics for t-tests of triplicate inconsistency error between different starting amounts for each tested method are shown. Percent difference is calculated as the percentage that the first amount in the comparison is relative to the second, thus Fivepercent 100 ng data had 13.84% more error than the 250 ng data. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. Illumina did not show a significant difference in consistency between the tested starting amounts, (F = 1.44, p = 0.23). (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8310572
title: Additional file 13: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S10. Pairwise comparisons of batch error. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 33% less error than Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8310566
title: Additional file 12: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S9. Method comparison of the number of falsely detected isomiRs and the expression of those false isomiRs. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 181% more false isomiRs per sequence than Illumina and 26% less expression than those of Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. (XLSX 39 kb)
DOI: 10.6084/m9.figshare.8310572.v1
title: Additional file 13: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S10. Pairwise comparisons of batch error. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 33% less error than Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8310566.v1
title: Additional file 12: of Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

description: Table S9. Method comparison of the number of falsely detected isomiRs and the expression of those false isomiRs. Percent difference is calculated as the percent difference of the first method in the comparison relative to the second, thus Clontech had 181% more false isomiRs per sequence than Illumina and 26% less expression than those of Illumina. Percent difference was calculated using more precise means than the rounded means shown in the table. Significant findings are highlighted in yellow. (XLSX 39 kb)