Lisa Johnson at #ICG13: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
•Als PPTX, PDF herunterladen•
1 gefällt mir•588 views
Lisa Johnson's talk at the #ICG13 GigaScience Prize Track: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. Shenzhen, 26th October 2018
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Lisa Johnson at #ICG13: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
Ähnlich wie Lisa Johnson at #ICG13: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes (20)
Mehr von GigaScience, BGI Hong Kong
Mehr von GigaScience, BGI Hong Kong (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Lisa Johnson at #ICG13: Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
- 1. Re-assembly, quality evaluation, and annotation of 678 marine microbial eukaryotic reference transcriptomes Lisa K. Johnson, Harriet Alexander, C. Titus Brown Lab for Data Intensive Biology (DIB) University of California, Davis ICG-13 Session 6: GigaScience Prize Track October 25, 2018 @monsterbashseq ljcohen@ucdavis.edu
- 2. DNA sequencing technology has revolutionized the field of biology, “New Computational Era” • Now, limiting step is data analysis • New tools and approaches constantly available • What to do if: – New samples to add to the project? – New software tool is developed? Re-analysis of old data with new tools and methods is not a common practice. Should it be?
- 3. Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) - Standardized data set, 1 sequencing facility and library preparation - 678 Illumina PE 50 RNA sequence datasets, 1 TB raw data - Wide diversity spanning more than 40 phyla - Original assemblies by the U.S. National Center for Genome Resources (NCGR) Keeling et al. 2014 PMID: 24959919 Caron et al. 2016 PMID: 27867198
- 4. • Adapted from the Brown lab, “Eel Pond mRNA-seq Protocol”: http://eel-pond.readthedocs.io/en/latest/ Titus Brown, Camille Scott, and Leigh Sheneman • Dr. Tessa Pierce: https://github.com/dib-lab/eelpond (snakemake workflow) Johnson, LK; Alexander, H; Brown, CT. 2018. GigaScience. In press. https://www.biorxiv.org/content/early/2018/09/18/323576 Programmatically automated pipeline (Python) x 678 transcriptomes
- 5. Our re-assemblies have more contigs:
- 6. Re-assemblies generally contain most of the information in the NCGR assemblies Proportionofcontigs(CRB-BLAST)
- 7. Similar Open Reading Frame (ORF) and Benchmarks of Universal Single Copy Orthologs (BUSCO)
- 8. Camille Scott, www.camillescott.org/dammit ‘dammit’ annotation pipeline: Pfam, Rfam, OrthoDB After annotation, ~30% extra content appears real DIB NCGR Extra content
- 9. Most DIB assemblies have more unique content. Unique k-mers (k=25), unique word combinations Luiz Irber, HyperLogLog: https://doi.org/10.1101/056846 https://github.com/dib-lab/khmer
- 10. Dinophyta have more unique k-mers Can we detect phylogenetic differences in the assemblies? Unique k-mers = unique word combinations (k=25) *
- 11. Ciliophora have lower ORF percentages Can we detect phylogenetic differences in the assemblies? *
- 12. • Re-assembly with new tools can yield new results (and content!) • Automated and programmable pipelines can be used to process arbitrarily many samples and test new tools • Analyzing many samples using a common pipeline identifies taxon-specic trends Summary
- 13. Acknowledgements • Data Intensive Biology Lab –Camille Scott, Luiz Irber • MSU iCER hpcc • NSF-XSEDE, Jetstream cloud Photo by James Word
Hinweis der Redaktion
- Hi, my name is Lisa Johnson, I’m a PhD student at UC Davis in Titus Brown’s Data Intensive Biology lab tackling questions surrounding k-mer based sequence analysis. Thank you for this opportunity to speak today. I would like to first acknowledge my co-authors, Harriet Alexander and my advisor, Titus Brown.
- The Marine Microbial Eukaryotic Sequencing Project is a unique set of mRNA sequence data generated by a consortium of PIs who all got together and submitted their favorite marine microbial eukaryotes to one sequencing facility. These species represent 40 pelagic and endosymbiotic phyla, such dinoflagellates, ciliates, diatoms. They are both phylogenetically diverse and geographically diverse, collected from all over the world. This is a really exciting set of data for a few reasons, one is because it is one of the largest publicly available sets of RNA data with a standardized library preparation from different organisms with a total of about 1 TB of raw sequence data. Second, it’s purposefully built, not a metatranscriptome. We technically know who is supposed to be in this data set, so we are generating reference transcriptomes for all of these species, some of which have never had any reference transcriptomes or genomes before. Right after data were sequenced, the NCGR assembled the transcriptomes as references with their own pipeline, using the genome assembler ABySS with some modifications and post-processing for transcriptomes. ==================== Bottom panel, left to right: Elphidium margaritaceum http://zoology.bio.spbu.ru/Eng/Sci/Korsun/Foram2_E-margaritaceum.jpg 2. Acanthamoeba https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/Parasite140120-fig3_Acanthamoeba_keratitis_Figure_3B.png/220px-Parasite140120-fig3_Acanthamoeba_keratitis_Figure_3B.png 3. Gonyaulax spinifera http://www.sms.si.edu/IRLSpec/images/Gonyaulax_Lg.jpg 4. Asterionellopsis glacialis http://www.smhi.se/oceanografi/oce_info_data/plankton_checklist/diatoms/asterionellopsis_glacialis.gif 5. Tetraselmis http://cfb.unh.edu/phycokey/Choices/Chlorophyceae/unicells/flagellated/TETRASELMIS/Tetraselmis_06_500x345.jpg 6. Oxyrrhis marina http://cfb.unh.edu/phycokey/Choices/Dinophyceae/NonPS-dinos/OXYRRHIS/Oxyrrhis_04_300x246_marina.jpg 7. Alexandrium http://www.whoi.edu/cms/images/dfino/2006/6/Alexandrium_en_11187_26907.jpg 8. Pseudonitzschia https://upload.wikimedia.org/wikipedia/commons/5/5e/Pseudonitzschia2.jpg 9. Chlamydomonas https://web.mst.edu/~microbio/BIO221_2009/images_2009/chlamydomonas-3.jpg 10. Emiliania_huxleyi https://upload.wikimedia.org/wikipedia/commons/d/d9/Emiliania_huxleyi_coccolithophore_(PLoS).png 11. Symbiodinium http://www.personal.psu.edu/tcl3/index.html 12. Phaeocystis antarctica http://www.esf.edu/antarctica/images/Phaeo_montage2.jpg 13. Micromonas http://roscoff-culture-collection.org/sites/default/files/field/image/micromonas-colored-350_0.jpg 14. Karenia brevis http://www.sms.si.edu/irlspec/images/Kareni_brevis_2.jpg 15. Thalassiosira pseudonana http://genome.jgi.doe.gov/Thaps3/Tpseudonana.jpg 16. Ditylum_brightwellii https://cimt.pmc.ucsc.edu/images/HAB%20ID/diatom/Ditylum_brightwellii.jpg
- Our modularized pipeline, which I wrote in Python, attempts to address these issues. It takes metadata from any data set in NCBI as input and decides which samples to run. Raw sequence reads are downloaded from NCBI, quality trimmed, checked with fastqc, run through digital normalization, then assembled using the Trinity transcriptome assembler. I’m glossing over a lot of details here because there is not enough time, but if you are interested please see me after to talk. There is a tutorial also available, called the “Eel pond protocol”, which is open access and has a small subset of data to run through the steps of a de novo assembly with Trinity. A benefit of this pipeline to highlight is that you can pick up from where you left off if something crashes. As anyone who has used an institutional high performance computing cluster knows, stuff breaks, stops running. With this pipeline, if something stops, you can start it again. This data set pushes the limits of our high performance computing clusters with 1 TB raw data, in terms of storage and compute resources. This took more than 8,000 computing hours, We have found that the resources required for these >600 assemblies are not trivial, and should be a consideration when planning for a project of this size in the future.
- In evaluating our assemblies, it appears that our re-assemblies have more contigs. A contig is a linear prediction of a full transcript by the assembly software. In subsequent slides, I’ll be showing similar figures like this, so want to orient you first. On the y-axis is what we’re measuring – here it’s the number of contigs. This is a split violin plot showing the frequency distribution around the mean of each pipeline. In the blue on the right shows our re-assemblies, which I’ve labeled “DIB” because we’re the data intensive biology lab. In the gray on the left are assemblies from NCGR. The number on top in blue shows the numbers of assemblies where DIB has a higher value than NCGR or in gray where NCGR has a higher number. In this case, we see that there were more DIB assemblies with higher numbers of contigs in comparison to the NCGR. The mean of DIB is around 48,000 contigs, with some samples producing up to 190,000 contigs up here towards the tail of the distribution. While the mean of NCGR is around 25,000 contigs and fewer assemblies have high numbers of contigs, the highest is about 100,000. So, these differences were interesting for us – and we came up with some questions (click)
- In addition to have higher quality scores, there appears to be more content. The proportion of contigs from a comparison called a reciprocal best blast of NCGR vs. our DIB assemblies indicates that most of the content found in NCGR is also found in the DIB re-assemblies. But also that there is extra information in the DIB assemblies not found in the NCGR assemblies. This information was obtained by aligning the two assemblies against each other both ways. First with NCGR as the reference, then the reverse with DIB as the reference. Engage with audience: As you can see here…our peak is about 0.7, or 70%. This means that we’re capturing 70% of the content in the NCGR assemblies. On the other hand, NCGR assemblies capture about 50% of the content of our assemblies. The difference is about 20%. The ~20% difference between these 2 blast comparisons leads us to still question whether we have just assembled junk or if we actually have higher resolution assemblies.
- Orient audience to graphs: left ORF on Y axis Even though we have more contigs, the open reading frame protein coding regions detected is similar if not more tightly distributed towards the upper range. Most of the assemblies have slightly higher ORF content. And on the right are BUSCO percentages, which is a set of benchmarking universal single copy orthologs expected to be found in all eukaryotic transcriptomes, like housekeeping genes. While there are problems with using BUSCO scores as an absolute measurement of assembly quality, they can serve as a comparative metric relative to another pipeline. Our assemblies have a similar BUSCO content relative to NCGR. So, at least these haven’t gone down. The extra content we found is probably not all junk.
- In digging deeper into the extra content, this is a plot of ONLY this extra content in the blue part. Samples are across the x axis, sorted by the number of extra contigs on the y axis. (pause, let this sink in, take a drink or something) Highlighted in green is the number of these extra contigs that are actually annotated to a known gene. I annotated the re-assemblies using this really great tool out of our lab by Camille Scott called ‘dammit’. No, it’s not an acronym, it was named out of frustration: “Just annotate it, dammit!” The dammit pipeline uses the highly-curated Pfam and Rfam known protein domain databases as well as ORthoDB with conserved orthology domains. About 1/3 of the extra content has annotations.
- Here we are comparing the raw sequence content, regardless of annotation, in terms of the number of kmers or unique word combinations with a k length of 25. We see that our assemblies fall above the 1:1 expectation, meaning that our assemblies have more unique words compared to the NCGR assemblies. This is kind of like taking two versions of the same book and digesting them down into individual 25 letter words found in the book. We found that our assemblies have more unique words than NCGR. Therefore, we are able to answer that our assemblies probably have a bit more biologically-meaningful content
- To address our second question about whether we can detect phylogenetic differences in the assemblies, we took a look at some of the assembly metrics grouped by taxa. Explain figures: unique k-mers on the y, input reads on the x, colors indicate different taxa, plotting mean and stdev The Dinoflagellates appear to have more unique kmer content. This seems to make sense, knowing that Dinoflagellates have this steady-state gene expression thing going on, where they just keep expressing genes on and one, then regulate more at the translational level. As far as the software, it might be useful to incorporate strain-specific information like this into assembly software.
- Here again, colors are different taxonomic groupings, mean percentage of open reading frame predictions on the y, number of transcripts on the x We see here that Cilliate assemblies appear to have a lower open reading frame percentage. This is interesting since it has recently been found Ciliates have an alternative triplet codon dictionary, with codons normally encoding STOP serving a different purpose. Dinoflagellates here have this high open reading frame content, and lots of contigs. In this case, it is useful to know that our assembly evaluation tools might perform outside the range of what is normal for the organisms in question. The assemblies are not necessarily lower quality, but may be perceived as lower in quality because of cool and unique features like this.
- Strain-specific trends may lead to understanding how raw data content affects the overall assembly quality
- Thank the Moore Foundation first.