Find research datasets worth reusing

opengpl-3.0-or-laterJun 2024View details →

Tandem repeat catalog of the human genome generated from long-read assemblies

Allele sequences of polymorphic loci (VCF) and README for all version 2 (2.0 + 2.1) files

opencc-by-4.0Nov 2024View details →

The PODER cross-ancestry long-read RNA-seq dataset

Table legends: <ul> <li>column_descriptions.docx: Extended description of columns in selected tables.</li> <li>00_sample_metadata: Sample and experimental metadata pertaining to each LR-RNA-seq sample.</li> <li>01_uma_gtf: Unfiltered merged annotation (UMA) GTF for all spliced intron chains discovered by each of the tools.</li> <li>02_uma_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in the UMA annotation used to filter transcripts.</li> <li>03_poder_gtf: Final PODER GTF, including predicted CDSs for novel transcripts and annotated CDSs for known transcripts.</li> <li>04_poder_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in PODER.</li> <li>06_poder_t_counts: PODER transcript counts in each sample as quantified by lr-kallisto.</li> <li>07_poder_g_counts: PODER gene counts in each sample as quantified by lr-kallisto.</li> <li>08_mage_t_counts_poder: Transcript counts for the MAGE RNA-seq dataset computed using the PODER annotation with kallisto.</li> <li>09_mage_t_counts_gencode: Transcript counts for the MAGE RNA-seq dataset computed using the GENCODE annotation with kallisto.</li> <li>10_mage_t_counts_enh: Transcript counts for the MAGE RNA-seq dataset computed using the Enhanced GENCODE annotation with kallisto.</li> <li>11_astu: Allele-specific transcript usage results.</li> <li>12_ase: Allele-specific expression results.</li> <li>13_mage_tau: Tau values for Enhanced GENCODE transcripts computed on the MAGE RNA-seq dataset.</li> <li>14_gwas_enrichments: GWAS enrichment results for ASTU genes overlapping GWAS genes.</li> <li>16_inter_catalog_overlap: Boolean detection of transcripts across PODER, annotations (GENCODE, RefSeq) and other RNA-seq / LR-RNA-seq-derived transcript catalogs (CHESS, GTEx, ENCODE4).</li> <li>17_personalized_hg38: Isoforms and their intron chains detected using personalized-GRCh38s.</li> <li>18_enh_gencode: Enhanced GENCODE GTF with novel transcripts from PODER added to all annotated transcripts from GENCODE v47</li> </ul>

opencc-by-4.0Mar 2024View details →

Companion data deposit of manuscript: Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies

This upload contains the metagenome assemblies and their binning results generated & described in the manuscript "Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies" (preprint version: arxiv2210.00098, "Towards complete representation of bacterial contents in metagenomic samples").  Mapping of sample names in the file names and the descriptors used as in the manuscript can be found in table S1, which is available along with the manuscript and also included in the supplementary_tables_and_figures tar archive here.

opencc-by-4.0Apr 2024View details →

Long-read sequencing reveals extensive gut phageome structural variations driven by genetic exchange with bacterial hosts

Genetic variations are instrumental for unraveling phage evolution and deciphering their functional implications. Here we explore the underlying fine-scale genetic variations in the gut phageome, especially structural variations (SVs). By employing virome-enriched long-read metagenomics sequencing across 91 individuals, we identified a total of 14,438 non-redundant phage SVs, and revealed their prevalence within the human gut phageome. These SVs are mainly enriched in genes involved in recombination, DNA methylation, and antibiotic resistance. Strikingly, a substantial fraction of phage SV sequences share close homology with bacterial fragments, with most SVs enriched for horizontal gene transfer (HGT) mechanism. Further investigations showed that these SV sequences were genetic exchanged between specific phage-bacteria pairs, particularly between phages and their respective bacterial hosts. Temperate phages exhibits a higher frequency of genetic exchange with bacterial chromosomes then virulent phages. Collectively, our findings provide novel insights into the genetic landscape of the human gut phageome.

opencc-by-4.0Nov 2024View details →

LoLiPoP-IoT TU/e Dataset: Preferred Illuminance at Different Correlated Color Temperatures for Paper- and Tablet-based Reading Tasks in an Office Environment

The dataset has the following 576 (=i.e., 32 Participants × 3 CCTs × 2 task media × 3 repetitions) preferred illuminance values (unit: lx). <table> <tbody> <tr> <td> </td> <td>Paper</td> <td>Paper</td> <td>Paper</td> <td>Paper</td> <td>Paper</td> <td>Paper</td> <td>Paper</td> <td>Paper</td> <td>Paper</td> </tr> <tr> <td>Participant ID</td> <td>CCT 1 (3000 K)</td> <td>CCT 1 (3000 K)</td> <td>CCT 1 (3000 K)</td> <td>CCT 2 (4000 K)</td> <td>CCT 2 (4000 K)</td> <td>CCT 2 (4000 K)</td> <td>CCT 3 (6000 K)</td> <td>CCT 3 (6000 K)</td> <td>CCT 3 (6000 K)</td> </tr> <tr> <td>1</td> <td>1098.00</td> <td>1668.00</td> <td>452.70</td> <td>396.20</td> <td>812.70</td> <td>589.60</td> <td>1139.00</td> <td>603.40</td> <td>502.60</td> </tr> <tr> <td>2</td> <td>275.90</td> <td>322.20</td> <td>316.00</td> <td>455.60</td> <td>412.30</td> <td>387.40</td> <td>382.30</td> <td>346.10</td> <td>411.10</td> </tr> <tr> <td>3</td> <td>2872.00</td> <td>399.70</td> <td>304.60</td> <td>1814.00</td> <td>339.80</td> <td>574.70</td> <td>1750.00</td> <td>725.60</td> <td>442.20</td> </tr> <tr> <td>4</td> <td>843.20</td> <td>686.40</td> <td>1088.00</td> <td>755.40</td> <td>1001.00</td> <td>818.20</td> <td>817.10</td> <td>790.70</td> <td>810.40</td> </tr> <tr> <td>5</td> <td>541.00</td> <td>404.10</td> <td>561.10</td> <td>513.50</td> <td>380.20</td> <td>419.70</td> <td>335.20</td> <td>343.30</td> <td>459.10</td> </tr> <tr> <td>6</td> <td>308.30</td> <td>289.90</td> <td>237.20</td> <td>485.20</td> <td>437.00</td> <td>403.90</td> <td>385.90</td> <td>462.80</td> <td>478.00</td> </tr> <tr> <td>7</td> <td>1111.00</td> <td>1531.00</td> <td>1280.00</td> <td>1422.00</td> <td>1273.00</td> <td>1812.00</td> <td>1408.00</td> <td>1618.00</td> <td>1734.00</td> </tr> <tr> <td>8</td> <td>744.20</td> <td>545.00</td> <td>462.90</td> <td>605.50</td> <td>616.00</td> <td>575.10</td> <td>1020.00</td> <td>721.20</td> <td>661.10</td> </tr> <tr> <td>9</td> <td>422.50</td> <td>707.40</td> <td>363.40</td> <td>460.30</td> <td>514.00</td> <td>353.40</td> <td>433.00</td> <td>463.80</td> <td>481.60</td> </tr> <tr> <td>10</td> <td>833.00</td> <td>626.80</td> <td>595.60</td> <td>773.60</td> <td>727.40</td> <td>363.40</td> <td>746.90</td> <td>723.70</td> <td>214.30</td> </tr> <tr> <td>11</td> <td>379.20</td> <td>689.10</td> <td>703.60</td> <td>526.80</td> <td>711.30</td> <td>981.40</td> <td>431.40</td> <td>511.20</td> <td>873.10</td> </tr> <tr> <td>12</td> <td>683.30</td> <td>764.60</td> <td>1124.00</td> <td>995.10</td> <td>990.10</td> <td>1116.00</td> <td>901.10</td> <td>962.50</td> <td>1296.00</td> </tr> <tr> <td>13</td> <td>318.30</td> <td>406.10</td> <td>492.20</td> <td>424.80</td> <td>531.40</td> <td>549.80</td> <td>510.70</td> <td>471.70</td> <td>529.20</td> </tr> <tr> <td>14</td> <td>228.80</td> <td>200.40</td> <td>233.30</td> <td>326.00</td> <td>322.30</td> <td>342.00</td> <td>425.40</td> <td>337.70</td> <td>411.10</td> </tr> <tr> <td>15</td> <td>252.10</td> <td>296.60</td> <td>293.60</td> <td>323.90</td> <td>318.40</td> <td>351.70</td> <td>328.10</td> <td>321.50</td> <td>572.70</td> </tr> <tr> <td>16</td> <td>473.70</td> <td>682.90</td> <td>709.00</td> <td>468.60</td> <td>855.50</td> <td>896.50</td> <td>611.00</td> <td>1165.00</td> <td>846.30</td> </tr> <tr> <td>17</td> <td>388.20</td> <td>266.90</td> <td>300.00</td> <td>386.30</td> <td>404.30</td> <td>372.30</td> <td>383.60</td> <td>364.20</td> <td>370.60</td> </tr> <tr> <td>18</td> <td>626.90</td> <td>740.70</td> <td>891.80</td> <td>644.60</td> <td>815.60</td> <td>938.50</td> <td>541.50</td> <td>558.20</td> <td>780.10</td> </tr> <tr> <td>19</td> <td>1025.00</td> <td>558.70</td> <td>535.70</td> <td>1056.00</td> <td>747.30</td> <td>1608.00</td> <td>819.70</td> <td>1092.00</td> <td>1259.00</td> </tr> <tr> <td>20</td> <td>661.80</td> <td>533.20</td> <td>464.60</td> <td>1093.00</td> <td>861.80</td> <td>1070.00</td> <td>894.60</td> <td>738.40</td> <td>1459.00</td> </tr> <tr> <td>21</td> <td>466.60</td> <td>401.70</td> <td>455.00</td> <td>542.90</td> <td>550.00</td> <td>491.30</td> <td>482.00</td> <td>540.60</td> <td>431.10</td> </tr> <tr> <td>22</td> <td>1093.00</td> <td>1037.00</td> <td>1106.00</td> <td>612.30</td> <td>777.20</td> <td>1041.00</td> <td>353.00</td> <td>848.10</td> <td>478.20</td> </tr> <tr> <td>23</td> <td>688.80</td> <td>1034.00</td> <td>746.90</td> <td>752.60</td> <td>1301.00</td> <td>817.80</td> <td>717.70</td> <td>730.20</td> <td>893.70</td> </tr> <tr> <td>24</td> <td>468.00</td> <td>231.50</td> <td>150.20</td> <td>666.90</td> <td>304.20</td> <td>282.40</td> <td>486.20</td> <td>319.30</td> <td>276.60</td> </tr> <tr> <td>25</td> <td>646.80</td> <td>1064.00</td> <td>743.10</td> <td>1072.00</td> <td>996.10</td> <td>961.20</td> <td>623.60</td> <td>1203.00</td> <td>1026.00</td> </tr> <tr> <td>26</td> <td>307.90</td> <td>224.70</td> <td>255.40</td> <td>410.10</td> <td>331.50</td> <td>331.80</td> <td>259.00</td> <td>354.60</td> <td>311.10</td> </tr> <tr> <td>27</td> <td>609.00</td> <td>661.80</td> <td>526.00</td> <td>451.50</td> <td>496.20</td> <td>557.70</td> <td>421.30</td> <td>475.60</td> <td>594.70</td> </tr> <tr> <td>28</td> <td>456.40</td> <td>479.60</td> <td>412.40</td> <td>458.60</td> <td>314.70</td> <td>349.90</td> <td>445.40</td> <td>331.80</td> <td>445.80</td> </tr> <tr> <td>29</td> <td>546.40</td> <td>921.10</td> <td>872.40</td> <td>906.50</td> <td>1001.00</td> <td>873.70</td> <td>576.90</td> <td>897.10</td> <td>840.70</td> </tr> <tr> <td>30</td> <td>438.90</td> <td>398.30</td> <td>372.90</td> <td>559.30</td> <td>344.60</td> <td>475.30</td> <td>359.90</td> <td>314.90</td> <td>403.50</td> </tr> <tr> <td>31</td> <td>667.10</td> <td>554.10</td> <td>449.70</td> <td>737.10</td> <td>730.10</td> <td>589.60</td> <td>789.00</td> <td>474.60</td> <td>603.80</td> </tr> <tr> <td>32</td> <td>1234.00</td> <td>1198.00</td> <td>1007.00</td> <td>849.00</td> <td>1158.00</td> <td>770.00</td> <td>909.70</td> <td>989.80</td> <td>726.70</td> </tr> </tbody> </table>     <table> <tbody> <tr> <td>Participant ID</td> <td>Tablet</td> <td>Tablet</td> <td>Tablet</td> <td>Tablet</td> <td>Tablet</td> <td>Tablet</td> <td>Tablet</td> <td>Tablet</td> <td>Tablet</td> </tr> <tr> <td> </td> <td>CCT 1 (3000 K)</td> <td>CCT 1 (3000 K)</td> <td>CCT 1 (3000 K)</td> <td>CCT 2 (4000 K)</td> <td>CCT 2 (4000 K)</td> <td>CCT 2 (4000 K)</td> <td>CCT 3 (6000 K)</td> <td>CCT 3 (6000 K)</td> <td>CCT 3 (6000 K)</td> </tr> <tr> <td>1</td> <td>154.80</td> <td>149.50</td> <td>98.75</td> <td>630.00</td> <td>288.90</td> <td>304.10</td> <td>181.80</td> <td>192.80</td> <td>199.60</td> </tr> <tr> <td>2</td> <td>179.40</td> <td>225.40</td> <td>241.00</td> <td>242.60</td> <td>316.80</td> <td>372.50</td> <td>206.40</td> <td>333.80</td> <td>275.20</td> </tr> <tr> <td>3</td> <td>681.70</td> <td>662.10</td> <td>703.70</td> <td>484.90</td> <td>662.70</td> <td>468.90</td> <td>2085.00</td> <td>537.40</td> <td>385.70</td> </tr> <tr> <td>4</td> <td>632.10</td> <td>1171.00</td> <td>998.30</td> <td>613.10</td> <td>1012.00</td> <td>818.40</td> <td>678.30</td> <td>693.70</td> <td>675.30</td> </tr> <tr> <td>5</td> <td>421.70</td> <td>349.30</td> <td>234.40</td> <td>392.50</td> <td>303.40</td> <td>280.20</td> <td>307.20</td> <td>428.70</td> <td>268.00</td> </tr> <tr> <td>6</td> <td>217.40</td> <td>206.00</td> <td>198.10</td> <td>324.40</td> <td>304.40</td> <td>343.70</td> <td>283.70</td> <td>285.70</td> <td>293.10</td> </tr> <tr> <td>7</td> <td>992.50</td> <td>1338.00</td> <td>1807.00</td> <td>1849.00</td> <td>1301.00</td> <td>1423.00</td> <td>1637.00</td> <td>1442.00</td> <td>1700.00</td> </tr> <tr> <td>8</td> <td>840.20</td> <td>1131.00</td> <td>778.60</td> <td>891.20</td> <td>1019.00</td> <td>651.80</td> <td>810.80</td> <td>1016.00</td> <td>866.20</td> </tr> <tr> <td>9</td> <td>391.40</td> <td>337.40</td> <td>355.70</td> <td>314.90</td> <td>510.80</td> <td>463.60</td> <td>209.80</td> <td>450.60</td> <td>435.30</td> </tr> <tr> <td>10</td> <td>493.40</td> <td>535.00</td> <td>691.80</td> <td>657.60</td> <td>740.50</td> <td>342.60</td> <td>1710.00</td> <td>781.00</td> <td>339.60</td> </tr> <tr> <td>11</td> <td>361.70</td> <td>277.90</td> <td>431.00</td> <td>342.80</td> <td>327.80</td> <td>337.60</td> <td>350.40</td> <td>237.20</td> <td>308.90</td> </tr> <tr> <td>12</td> <td>857.10</td> <td>1114.00</td> <td>1348.00</td> <td>1302.00</td> <td>1408.00</td> <td>1294.00</td> <td>1031.00</td> <td>1142.00</td> <td>1078.00</td> </tr> <tr> <td>13</td> <td>371.80</td> <td>358.20</td> <td>731.40</td> <td>320.10</td> <td>459.50</td> <td>360.00</td> <td>440.40</td> <td>507.10</td> <td>436.90</td> </tr> <tr> <td>14</td> <td>361.20</td> <td>267.00</td> <td>285.80</td> <td>414.80</td> <td>398.70</td> <td>365.90</td> <td>411.50</td> <td>381.50</td> <td>392.30</td> </tr> <tr> <td>15</td> <td>376.00</td> <td>297.80</td> <td>294.20</td> <td>326.20</td> <td>330.50</td> <td>351.70</td> <td>327.90</td> <td>397.00</td> <td>357.00</td> </tr> <tr> <td>16</td> <td>221.20</td> <td>557.40</td> <td>536.30</td> <td>885.40</td> <td>518.90</td> <td>688.50</td> <td>192.40</td> <td>703.50</td> <td>568.90</td> </tr> <tr> <td>17</td> <td>435.70</td> <td>479.40</td> <td>444.50</td> <td>391.00</td> <td>452.90</td> <td>426.60</td> <td>421.40</td> <td>390.60</td> <td>397.80</td> </tr> <tr> <td>18</td> <td>431.50</td> <td>728.60</td> <td>985.70</td> <td>319.40</td> <td>542.10</td> <td>890.40</td> <td>326.90</td> <td>696.70</td> <td>575.80</td> </tr> <tr> <td>19</td> <td>470.80</td> <td>586.10</td> <td>476.90</td> <td>667.10</td> <td>570.10</td> <td>614.20</td> <td>803.80</td> <td>638.80</td> <td>679.50</td> </tr> <tr> <td>20</td> <td>186.10</td> <td>386.50</td> <td>335.60</td> <td>457.40</td> <td>471.20</td> <td>370.90</td> <td>324.70</td> <td>292.60</td> <td>273.40</td> </tr> <tr> <td>21</td> <td>420.80</td> <td>494.30</td> <td>447.60</td> <td>500.80</td> <td>470.30</td> <td>613.00</td> <td>581.80</td> <td>411.40</td> <td>361.40</td> </tr> <tr> <td>22</td> <td>510.20</td> <td>221.60</td> <td>228.60</td> <td>300.80</td> <td>285.50</td> <td>282.30</td> <td>477.30</td> <td>326.50</td> <td>185.20</td> </tr> <tr> <td>23</td> <td>202.20</td> <td>294.20</td> <td>252.00</td> <td>261.60</td> <td>308.80</td> <td>259.90</td> <td>476.10</td> <td>157.80</td> <td>313.10</td> </tr> <tr> <td>24</td> <td>464.10</td> <td>477.30</td> <td>516.50</td> <td>629.00</td> <td>516.70</td> <td>568.30</td> <td>842.50</td> <td>507.90</td> <td>613.20</td> </tr> <tr> <td>25</td> <td>451.80</td> <td>288.20</td> <td>444.10</td> <td>317.20</td> <td>326.40</td> <td>324.20</td> <td>456.40</td> <td>299.80</td> <td>294.80</td> </tr> <tr> <td>26</td> <td>157.40</td> <td>104.90</td> <td>198.70</td> <td>343.70</td> <td>328.60</td> <td>316.30</td> <td>157.40</td> <td>210.90</td> <td>225.10</td> </tr> <tr> <td>27</td> <td>510.00</td> <td>488.90</td> <td>552.30</td> <td>467.70</td> <td>572.20</td> <td>511.20</td> <td>440.10</td> <td>587.80</td> <td>376.60</td> </tr> <tr> <td>28</td> <td>257.20</td> <td>195.70</td> <td>240.90</td> <td>275.90</td> <td>286.00</td> <td>269.70</td> <td>410.00</td> <td>219.40</td> <td>235.40</td> </tr> <tr> <td>29</td> <td>732.00</td> <td>614.40</td> <td>567.20</td> <td>1046.00</td> <td>664.60</td> <td>693.30</td> <td>711.40</td> <td>733.80</td> <td>645.80</td> </tr> <tr> <td>30</td> <td>280.90</td> <td>279.90</td> <td>381.70</td> <td>376.30</td> <td>305.10</td> <td>333.30</td> <td>199.20</td> <td>311.20</td> <td>456.50</td> </tr> <tr> <td>31</td> <td>501.40</td> <td>341.50</td> <td>365.80</td> <td>549.80</td> <td>442.70</td> <td>442.00</td> <td>697.00</td> <td>499.20</td> <td>449.20</td> </tr> <tr> <td>32</td> <td>1039.00</td> <td>1198.00</td> <td>1256.00</td> <td>993.80</td> <td>1129.00</td> <td>1166.00</td> <td>916.50</td> <td>858.70</td> <td>881.30</td> </tr> </tbody> </table>

opencc-by-4.0Mar 2024View details →

Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"

Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads". The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including: <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>

opencc-by-4.0Dec 2024View details →

Simulation data for benchmarking de novo long read transcriptome assembly software

Method of simulation of differentially expressed biological replicates We first obtained a subset of transcripts that are widely expressed in the GTEx v9 dataset (92 samples) using Gencode comprehensive annotation (v44). We kept transcripts with more than 5 reads in at least 15 samples after Salmon quantification (18145 genes, 40509 transcripts), and stored their mean count per million (CPM) values as the control group’s baseline expression. We then generated a perturbed set of CPM values where transcript expression was changed by: (1) randomly selecting 1000 genes and changing all transcripts belonging to that gene concordantly (500 genes 2 fold up and 500 genes 2 fold down), (2) selected another 1000 genes randomly, and then select 2 random transcripts from the gene and swap their expression, (3) selected another 1000 genes randomly, and then select 1 random transcript to change its expression (500 transcripts 2 fold up and 500 transcripts 2 fold down). The updated CPM were stored as the perturbed group baseline expression. We then generated a count matrix and CPM matrix for 3 control replicates and 3 perturbed replicates with gamma distribution, followed by a Poisson distribution <a href="https://www.zotero.org/google-docs/?cUP4ui">(Baldoni et al., 2024)</a>. Both long-read and short-read FASTQ files were simulated using SQANTI-SIM with default settings and ONT R9.4 cDNA error profile (v 0.2.1) <a href="https://www.zotero.org/google-docs/?Qyopst">(Mestre-Tomás et al., 2023)</a>. The long read data contained 6 million reads in total, and an average read length of 1085 bp, and short read data was 100 bp paired-end. We then subsampled the short-read data to match the total number of base pairs in the long read data (6.5 billion bases). The simulated data was non-stranded, and contains 2000 DE genes, 2000 genes with DTU, 5927 transcripts with DTU and 6933 DE transcripts.

Long reads training material for 'Quality Control' tutorial (Galaxy Training Material)

The data provided here are part of a Galaxy Training Network tutorial for reads Quality Control. PacBio HiFi reads were provided by PacBio - GIAB sample HG002 (https://www.pacb.com/smrt-science/smrt-resources/datasets/) and was downsampled using seqtk (https://github.com/lh3/seqtk) Nanopore reads were provided by Tim Kahlke as part of "Long-Read, long reach Bioinformatics Tutorials" (https://timkahlke.github.io/LongRead_tutorials/) and was basecalled using Guppy v5.0.2 (dna_r9.4.1_450bps_sup.cfg).

Improved genome annotation of Rhynchosporium commune isolate UK7 using Illumina short reads of in vitro and in plantae conditions

Improved genome annotation of the Rhynchosporium commune isolate UK7 using Illumina short reads of in vitro and in plantae conditions. The short reads used for the annotation are available at <a href="https://doi.org/10.5281/zenodo.5729968">https://doi.org/10.5281/zenodo.5729968</a> and <a href="https://doi.org/10.5281/zenodo.5729863">https://doi.org/10.5281/zenodo.5729863</a>.To create the gene models, we used tophat v. 2.0.14 to align short reads to the UK7 reference genome (Trapnell et al., 2009). The Intron splice site hints were generated using bam2hints, included in the AUGUSTUS v. 3.2.1 software (Stanke et al., 2006). Due to the very high RNA-sequencing depth available, intron splice hints were filtered for a minimum coverage of 20 reads to avoid an impact of spurious splice signals on gene prediction. To produce ab initio gene models, the BRAKER v. 1.0 pipeline (Hoff et al., 2016) combining GeneMark-ET ab initio gene model predictions and AUGUSTUS v. 3.2.1. GeneMark-ET was trained using the RNA-seq-based splice information as hints. AUGUSTUS was automatically trained using ab initio gene models that were fully supported by splice information. Finally, AUGUSTUS was used to predict gene models using both RNA-seq splice information and coding sequence hints based on exonerate protein alignments as extrinsic evidence.

opencc-by-4.0Jan 2022View details →

Enhanced Protein Isoform Characterization Through Long-Read Proteogenomics - Workflow Results

<pre> </pre> The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis. Companion Repositories: <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> Companion Datasets <ol> <li><a href="https://zenodo.org/deposit/5703754">Long-Read-Proteogenomics Workflow Sample and Reference Data</a></li> <li><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></li> </ol> This Repository contains the complete output from the execution of the <a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow</a>, using the input from <a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a>.    The file jurkat.flnc.bam was 6.5 GB had to be split into 13 separate files and for use should be rejoined -- here are the steps that were used to split the file up.    1. Convert jurkat.flnc.bam (binary format) to sam file (text format) without header:  samtools view jurkat.flnc.bam > jurkat.flnc.sam 2. Capture the header: samtools view -H jurkat.flnc.bam > jurkat.flnc.header.sam 3. Split jurkat.flnc.sam into smaller files (aim to get final size under 2GB): split -l 400000 jurkat.flnc.sam jurkat.flnc.chunk. 4. Convert each of these files back to bam for uploading: samtools view -b jurkat.flnc.chunk.a* -o jurkat.flnc.chunk.a*.bam (*=a,b,c,d,e,f,g,h,i,j,k,l,m) After downloading, reverse this process including using the header file which is found in the LRPG-Manuscript-Results-results-results-jurkat-isoseq3-companion-files.tar.gz file> 1. Convert the bam files back to sam files: samtools view jurkat.flnc.chunk.a*.bam > jurkat.flnc.chunk.a*.sam (*=a,b,c,d,e,f,g,h,i,j,k,l,m) 2. Combine the header together with the sam files: cat jurkat.flnc.chunk.a*sam > jurkcat.flnc.sam (verified the same number of lines of the sam files is identical to the number of lines of the original without header: 4,956,761.  Header file is 13 lines. 3. Convert to bam files if desired: samtools view -b jurkat.flnc.sam -o jurkat.flnc.bam 4. Rehead with the header file: samtools reheader -P -i jurkat.flnc.header.sam jurkat.flnc.bam

opencc-by-4.0Jun 2022View details →

Long read proteogenomics to characterize protein isoform diversity in human umbilical vein endothelial cells (HUVECs)

Endothelial cells (ECs) comprise the lumenal lining of all blood vessels and are critical for the functioning of the cardiovascular system and their phenotypes can be modulated by protein isoforms. To characterize the isoform landscape within EC, we applied a long read proteogenomics approach to analyze human umbilical vein endothelial cells (HUVECs). Transcripts delineated from PacBio sequencing serve as the basis for a sample-specific protein database used for downstream MS analysis to infer protein isoform expression. We detected 53,836 transcript isoforms from 10,426 genes, with 22,195 of those transcripts being novel. Furthermore, the predominant isoform in HUVECs does not correspond with the accepted “reference isoform” 25% of the time, with vascular pathway-related genes among this group. We found 2,597 protein isoforms supported through unique peptides, with an additional 2,280 isoforms nominated upon incorporation of long-read transcript evidence. We characterized a novel alternative acceptor for endothelial-related gene CDH5, suggesting potential changes in its associated signaling pathways. Finally, we identified novel protein isoforms arising from a diversity of splicing mechanisms supported by uniquely mapped novel peptides. Our results represent a high resolution atlas of known and novel isoforms of potential relevance to endothelial phenotypes and function.

opencc-by-4.0Aug 2022View details →

CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading

Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files. Examples: <ul> <li>S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel <ul> <li>Z-Stack was not activated inside acquisition experiment</li> </ul> </li> <li>S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels <ul> <li>Z-Stack was activated inside acquisition experiment</li> </ul> </li> </ul> The test files (so far) contain not any data with more "advanced" dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet.

Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq

Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.

Introduction to the Future Blood Testing Network Plus - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)

This video is the first talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading). Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/  This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/YuBsU3NDdB0

"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)

This video is the tenth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022. "Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading) Bio: Al Edwards has a background in fundamental immunology combined with expertise in biochemical engineering, he is an interdisciplinary researcher focussed on solving current and future healthcare challenges using an engineering science approach that combines a range of fields from biology, biochemistry, chemistry and physics. He works at the interface between academic technology discovery and industrial development and have experience of both fundamental research and the commercialisation of new technology. The two main challenges he currently works on are the development of affordable microfluidics for clinical diagnostics and microbiology, and the engineering science of complex biologic therapeutics such as vaccines. Alexander's research is funded from a wide range of sources, including NIHR , EPSRC, SBRI Healthcare, the Wellcome Trust, Innovate UK and industry Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/21a78Vql8b0

Measuring platelet function: new strategies for precision medicine to prevent thrombosis - Prof Jon Gibbins (University of Reading)

This video is the third talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Measuring platelet function: new strategies for precision medicine to prevent thrombosis - Prof Jon Gibbins (University of Reading). Bio: Jon Gibbins is Professor of Cell Biology within the School of Biological Sciences at the University and is Director of the Institute for Cardiovascular and Metabolic Research. He is a graduate of the University, obtaining a degree in Pathobiology with Chemistry in 1991 and a PhD in Molecular Endocrinology in 1995. Following a period of postdoctoral research at the Oxford University, he returned to Reading in 1998 as a lecturer. Jon has established an internationally leading research group that studies blood clotting, with a particular focus on the development of more effective clinical strategies for the prevention and treatment of heart attacks and strokes, and thrombosis associated with infection. Jon values greatly working in an active, engaging and successful school, in which all aspects of biology are represented, and he champions cross-disciplinary working to approach today’s most challenging and pressing questions in new ways. He believes strongly in widening participation and improving levels of equity, diversity and inclusion across our institution. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/8vJZ_WO-dvk

Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).

This video is the first talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading). Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/I1xsig9C8w0

Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)

This video is the fifth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading) Bio: Beth Harvey is currently a master’s student in digital health and data analytics at the Henley Business School. Having completed her bachelor’s in biomedical science, Beth has then gone on to work in the medical device and IVD regulatory sector with a consultancy firm in Vancouver. After moving back to the UK in 2019 Beth worked with multiple UK manufacturers in the digital health space which spurred her interest in the field she is now studying. Beth’s research interests are in healthcare innovation remote patient monitoring, and data analysis. She is currently finishing her dissertation on the opportunities and challenges for remote blood monitoring in oncology in collaboration with the Future Blood Network. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/8pbO3N9IQKM

Collaborative Innovation Project funding launch - Dr Samantha Kanza (University of Reading, University of Southampton)

This video is the eighth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Collaborative Innovation Project funding launch - Dr Samantha Kanza (University of Reading) Bio: Dr Samantha Kanza is a Senior Enterprise Fellow at the University of Southampton. She completed her MEng in Computer Science at the University of Southampton and then worked for BAE Systems Applied Intelligence for a year before returning to do an iPhD in Web Science (in Computer Science and Chemistry), which focused on Semantic Tagging of Scientific Documents and Electronic Lab Notebooks. She was awarded her PhD in April 2018. Samantha works in the interdisciplinary research area of applying computer science techniques to the scientific domain, specifically through the use of semantic web technologies and artificial intelligence. Her research includes looking at electronic lab notebooks and smart laboratories, to improve the digitization and knowledge management of the scientific record using semantic web technologies; and using IoT devices in the laboratory. She has also worked on a number of interdisciplinary Semantic Web projects in different domains, including agriculture, chemistry and the social sciences. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/  This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/PDWZkZBzfqw

Communicating with the Network & Landscape Workshops - Dr Samantha Kanza (University of Reading, University of Southampton)

This video is the eleventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Communicating with the Network & Landscape Workshops - Dr Samantha Kanza (University of Southampton). Bio: Dr Samantha Kanza is an Enterprise Fellow at the University of Southampton. She completed her MEng in Computer Science at the University of Southampton and then worked for BAE Systems Applied Intelligence for a year before returning to do an iPhD in Web Science (in Computer Science and Chemistry), which focused on Semantic Tagging of Scientific Documents and Electronic Lab Notebooks. She was awarded her PhD in April 2018. Samantha works in the interdisciplinary research area of applying computer science techniques to the scientific domain, specifically through the use of semantic web technologies and artificial intelligence. Her research includes looking at electronic lab notebooks and smart laboratories, to improve the digitization and knowledge management of the scientific record using semantic web technologies; and using IoT devices in the laboratory. She has also worked on a number of interdisciplinary Semantic Web projects in different domains, including agriculture, chemistry and the social sciences. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/fvRb9ULGU2I