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LPHIG Bioinformatics of SFS Genomics Center Program Projects. Project leader: Chun-Yuan Huang 1 Members : Charles Joseph Murphy 1 , Aurash Mohaimani 2 PIs: Peter J. Tonellato 1,2,3 , Rebecca Klaper 4 1. Zilber School of Public Health, University of Wisconsin at Milwaukee, Milwaukee, WI

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lphig bioinformatics of sfs genomics center program projects

LPHIG Bioinformatics of SFS Genomics Center Program Projects

Project leader: Chun-Yuan Huang1

Members: Charles Joseph Murphy1, AurashMohaimani2

PIs: Peter J. Tonellato1,2,3, Rebecca Klaper4

1. Zilber School of Public Health, University of Wisconsin at Milwaukee, Milwaukee, WI

2. Medical Informatics Program, University of Wisconsin at Milwaukee, Milwaukee, WI

3. Center for Biomedical Informatics, Harvard Medical School, Boston, MA

4. Great Lakes Genomics Center, School of Freshwater Sciences, University of Wisconsin, Milwaukee, WI

sfs genomics center program projects
SFS Genomics Center Program Projects
  • Project 1: Biomarkers of Reproduction Staging of Sturgeon (Acipenser fulvescens)
  • Project 2: Daphnia Magna Gene Expression under Nanomaterials Exposure
background for conservation of sturgeon population
Background for conservation of sturgeon population
  • Sturgeon appeared in the fossil record 200 million years ago, and have undergone remarkably little morphological change, indicating their evolution has been exceptionally slow and earning them informal status as living fossils.
  • Sturgeon become prized lately for its meat, eggs (caviar) and oil.
  • Sturgeon was exceptionally vulnerable to overfishing, as restoration of its populations is complicated by its slow reproductive cycle. Sturgeon exhibits delayed sexual maturity (between 10 and 30 years of age), infrequent spawning (every few years), and sexual monomorphism.
background for sturgeon sex determination
Background for sturgeon sex determination
  • Searches for sex-specific markers using DNA-based techniques such as RAPD and AFLP had been failed [1].
  • Two sturgeon sex determining genes, dmrt1 (human homolog: doublesexand Mab-3 related transcription factor 1) and tra-1, were identified using next-generation 454 sequencing and de novo assembly of gonad transcriptomes [2].
  • Sturgeons undergoing male differentiation express high levels (by qPCR analysis) of Sertoli cell factors (dmrt1, sox9) and of genes involved in the production and receptivity of androgens (cyp17a1, star and ar) together with lh [3].

[1] SaeedKeyvanshokooh and Ahmad Gharaei. A review of sex determination and searches for sex-specific markers in sturgeon. Aquaculture Research, 2010 Aug;41(9):e1–e7.

[2] Hale MC, Jackson JR, Dewoody JA. Discovery and evaluation of candidate sex-determining genes and xenobiotics in the gonads of lake sturgeon (Acipenser fulvescens). Genetica. 2010 Jul;138(7):745-56.

[3] Berbejillo J, et al. Expression and phylogeny of candidate genes for sex differentiation in a primitive fish species, the Siberian sturgeon, Acipenser baerii. MolReprod Dev. 2012 Aug;79(8):504-16.

background for sturgeon reproduction stage
Background for sturgeon reproduction stage
  • Determination of reproduction stages would help to detect gonadal maturity for sturgeon reproduction and population conservation.
  • Reproduction stages are defined by DNR [1] as follows:
    • Stage 1
    • Stage 2
    • Stage 3
    • Stage 4

[1] N.A.

biomarkers of reproduction staging of sturgeon
Biomarkers of Reproduction Staging of Sturgeon

Primary goals:

  • Use RNA-seq to compare multiple sexual stages and determine uniquely expressed genes among each stage which could potentially be used as a biomarker for reproduction stage determination.
  • Use the above RNA-seq annotated gene information to identify proteins in our proteomics data obtained from sturgeon blood of various stages.
  • Use the above RNA-seq annotated gene information to examine the evolutionary questions regarding how the genome of sturgeon is relates to other more recently evolved fish species.
slide7

Sturgeon

  • Gene expression biomarkers for sexual stages

RNA-Seq

  • Annotate proteomics data from blood samples
  • Phylogenic analysis
preliminary considerations
Preliminary Considerations
  • No reference genome is available for lake sturgeon.
  • Ongoing and finished fish genomes:
    • Pufferfish (Tetraodonnigroviridis)
    • Fugu (Japanese Pufferfish)
    • Stickleback (Gasterosteusaculeatus)
    • Coelocanth(Indonesia), Coelocanth (South African)
    • Tilapia (family Cichlidae) Genome Project that includes Nile Tilapia (Oreochromisniloticus), Astatotilapiaburtoni, Pundamilianyererei, Malawi zebra, Neolamprologusbrichardi
    • Zebrafish
    • Salmon
    • Catfish
    • Medakaricefish
    • Lamprey (Lampetrafluviatilis)
    • Dogfish (Scyliorhinuscanicula)
    • Southern platyfish (Xiphophorusmaculatus)
    • Poeciliidfish (Xiphophorusmaculatus)
    • Spotted gar (Lepisosteusoculatus)
preliminary considerations cont
Preliminary Considerations (cont.)
  • Sturgeon is one of the oldest families of bony fish (ray-finned fish, class of Actinopterigii) in existence, and is quite distant from other fishes that have been sequenced.
  • Based on Near [1], Sturgeon seems closer to Gar than to the other fishes.
  • A Spotted Gar’s transcriptome (by RNA-Seq) is constructed by Amores [2] and available from DDBJ [3], while its draft genome assembly is available from Broad Institute [4].
  • ,zebrafish

[1] Near et al. Resolution of ray-finned fish phylogeny and timing of diversification. ProcNatlAcadSci U S A. 2012 Aug 21;109(34):13698-703.

[2] Amores et al, Genome evolution and meiotic maps by massively parallel DNA sequencing: spotted gar, an outgroup for the teleost genome duplication. Genetics. 2011 Aug;188(4):799-808.

[3] https://trace.ddbj.nig.ac.jp/DRASearch/submission?acc=SRA026509

[4] ftp://ftp.broadinstitute.org/pub/assemblies/fish/spottedGar/

slide10

Slide adapted from “Leveraging Trinity for de novo transcriptome assembly and analysis, 2012 CSHL workshop, Brian Haas, Broad Institute

slide11

Overview of the de novo transcriptome assembly strategy

Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011 Sep 7;12(10):671-82.

survey on de novo transcriptome assembly methods
Survey on de novo transcriptome assembly methods

Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011 Sep 7;12(10):671-82.

sample description
Sample Description
  • Sturgeon liver samples from three biological replicates of female reproduction stage 1, 2, and male reproduction stages stage 1, 2.
  • 100-bp paired-end reads from Illumina HiSeq2500.
  • 915,602,572 total reads (457,801,286 paired reads)
  • The Purdue Genomics Center has done the de novo Transcriptome assembly using the Trinity method.
  • The reads have been further mapped to contigs (coordinate) via Bowtie, sorted by coordinate and resulted in BAM-format files for each sample.
  • Also contigs in each sample are counted for all mapped reads, along with the contig’slength (bp), the homolog search result of each contig blasted against NCBI, the homolog’s GO terms and GenbankIDs.http://www.genomics.purdue.edu/%7Ecore/projects/Klaper/
sample description1
Sample Description

Note: F1L: female stage 1, F2: female stage 2, M1: male stage 1, M2: male stage 2. Samples are all prepared from sturgeon liver (L).

example of data fastqc
Example of Data FastQC
  • Data is in high quality. Unfiltered reads:
slide17

Slide adapted from “Leveraging Trinity for de novo transcriptome assembly and analysis, 2012 CSHL workshop, Brian Haas, Broad Institute

analysis plan a the fast track version
Analysis Plan A (the fast-track version)
  • Use contigs, counts and homolog (blast result of the contigs) available from the Purdue Genomics Center
  • Differential expression and biomarker discovery
    • Estimate contigs abundance using bowtie (done in Purdue U.)
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR:
      • Identify DE contigs/biomarkers among reproduction stages (F1 vs. F2; M1 vs. M2)
      • Identify DE contigs/biomarkers among gender (F1 vs. M1; F2 vs. M2)
    • Functional annotation by Trinotate (a module in the Trinity package).
    • Pathway analysis by Ingenuity Pathway Analysis (IPA).
    • GO term enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery (DAVID)
    • Gene Set Enrichment Analysis (GSEA)
  • Identify proteins in the proteomics data obtained from sturgeon blood of various stages.
  • Comparative genomic study – evolutionary aspect of Sturgeon genome as relates to other fish species
slide19

Sturgeon F1 vs. F2 DE contigs (isoforms) analysis by bowtie-edgeR

Red: FDR<0.05, total 309 contigs

slide20

Sturgeon M1 vs. M2 DE contigs (isoforms) analysis by bowtie-edgeR

Red: FDR<0.05, total 229 contigs

slide21

Sturgeon F1 vs. M1 DE contigs (isoforms) analysis by bowtie-edgeR

Red: FDR<0.05, total 616 contigs

slide22

Sturgeon F2 vs. M2 DE contigs (isoforms) analysis by bowtie-edgeR

Red: FDR<0.05, total 1543 contigs

analysis plan a the fast track version1
Analysis Plan A (the fast-track version)
  • Use contigs, counts and homolog (blast result of the contigs) available from the Purdue Genomics Center
  • Differential expression and biomarker discovery
    • Estimate contigs abundance using RSEM
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR:
      • Identify DE contigs/biomarkers among reproduction stages (F1 vs. F2; M1 vs. M2)
      • Identify DE contigs/biomarkers among gender (F1 vs. M1; F2 vs. M2)
    • Functional annotation by Trinotate (a module in the Trinity package).
    • Pathway analysis by Ingenuity Pathway Analysis (IPA).
    • GO term enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery (DAVID)
    • Gene Set Enrichment Analysis (GSEA)
  • Identify proteins in the proteomics data obtained from sturgeon blood of various stages.
  • Comparative genomic study – evolutionary aspect of Sturgeon genome as relates to other fish species
slide24

Sturgeon F1 vs. F2 DE contigs (isoforms) analysis by RSEM-edgeR

Red: FDR<0.05, total 27 contigs

slide25

Sturgeon F1 vs. F2: 27 DE contigs (isoforms) sorted by logFoldChange

(total 27 contigs with FDR < 0.05)

slide26

Sturgeon M1 vs. M2 DE contigs (isoforms) analysis by RSEM-edgeR

Red: FDR<0.05, total 4 contigs

slide27

Sturgeon M1 vs. M2: 4 DE contigs (isoforms)

(total 4 contigs with FDR < 0.05)

slide28

Sturgeon F1 vs. M1 DE contigs (isoforms) analysis by RSEM-edgeR

Red: FDR<0.05, total 0 contigs

slide29

Sturgeon F2 vs. M2 DE contigs (isoforms) analysis by RSEM-edgeR

Red: FDR<0.05, total 70 contigs

slide30

Sturgeon F2 vs. M2: 70 DE contigs (isoforms) sorted by logFoldChange

(total 70 contigs with FDR < 0.05)

slide31

Sturgeon F2 vs. M2: 70 DE contigs (isoforms) sorted by logFoldChange

(total 70 contigs with FDR < 0.05) (continue ..)

slide32

Sturgeon F1 vs. F2 DE components (genes) analysis by RSEM-edgeR

Red: FDR<0.05, total 5 components

slide33

Sturgeon F1 vs. F2: 5 DE components (genes)

(total 5 components with FDR < 0.05)

slide34

Sturgeon M1 vs. M2 DE components (genes) analysis by RSEM-edgeR

Red: FDR<0.05, total 7 components

slide35

Sturgeon M1 vs. M2: 7 DE components (genes)

(total 7 components with FDR < 0.05)

slide36

Sturgeon F1 vs. M1 DE components (genes) analysis by RSEM-edgeR

Red: FDR<0.05, total 10 components

slide37

Sturgeon F1 vs. M1: 10 DE components (genes)

(total 10 components with FDR < 0.05)

slide38

Sturgeon F2 vs. M2 DE components (genes) analysis by RSEM-edgeR

Red: FDR<0.05, total 49 components

slide39

Sturgeon F2 vs. M2: 49 DE components (genes) sorted by logFoldChange (total 49 components with FDR < 0.05)

slide40

Sturgeon F2 vs. M2: 49 DE components (genes) sorted by logFoldChange (total 49 components with FDR < 0.05) (continue ..)

analysis plan a the fast track version2
Analysis Plan A (the fast-track version)
  • Use contigs, counts and homolog (blast result of the contigs) available from the Purdue Genomics Center
  • Differential expression and biomarker discovery
    • Estimate contigs abundance using RSEM
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR:
      • Identify DE contigs/biomarkers among reproduction stages (F1 vs. F2; M1 vs. M2)
      • Identify DE contigs/biomarkers among gender (F1 vs. M1; F2 vs. M2)
    • Functional annotation by Trinotate (a module in the Trinity package).
    • Pathway analysis by Ingenuity Pathway Analysis (IPA).
    • GO term enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery (DAVID)
    • Gene Set Enrichment Analysis (GSEA)
  • Identify proteins in the proteomics data obtained from sturgeon blood of various stages.
  • Comparative genomic study – evolutionary aspect of Sturgeon genome as relates to other fish species
functional annotation by trinotate
Functional annotation by Trinotate
  • Trinotateis a comprehensive annotation suite designed for automatic functional annotation of de novo Transcriptome assemblies created using the Trinity assembly program.
  • Trinotate makes use of a number of different well referenced methods for functional annotation including
    • Search/generate the most likely longest-ORF peptide candidates from the contigs of the Trinity Assembly (Transdecoder)
    • Homology search to known sequence data (NCBI-BLASTP),
    • Protein domain identification (HMMER/PFAM),
    • Protein signal prediction (singalP/tmHMM), and
    • Comparison to currently currated annotation databases (EMBL UniproteggNOG/GO Pathways databases).
functional annotation by trinotate1
Functional annotation by Trinotate
  • Trinity de novo assembled isoforms (446,408) are subject to Trinotate analysis.
  • More than one peptide could be resulted from blastp of one contig.
  • Trinotate annotation of 446,408 contigs become 478,700 peptide records.
analysis plan a the fast track version3
Analysis Plan A (the fast-track version)
  • Use contigs, counts and homolog (blast result of the contigs) available from the Purdue Genomics Center
  • Differential expression and biomarker discovery
    • Estimate contigs abundance using RSEM
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR:
      • Identify DE contigs/biomarkers among reproduction stages (F1 vs. F2; M1 vs. M2)
      • Identify DE contigs/biomarkers among gender (F1 vs. M1; F2 vs. M2)
    • Functional annotation by Trinotate (a module in the Trinity package).
    • Pathway analysis by Ingenuity Pathway Analysis (IPA).
    • GO term enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery (DAVID)
    • Gene Set Enrichment Analysis (GSEA)
  • Identify proteins in the proteomics data obtained from sturgeon blood of various stages.
  • Comparative genomic study – evolutionary aspect of Sturgeon genome as relates to other fish species
ipa analysis of de isoforms from f2 vs m2
IPA analysis of DE isoforms from F2 vs. M2
  • UniProt_ID of DE isoforms from F2_vs_M2 are used for IPA analysis.
  • There are 46 non-redundant UniProt_IDs from 70 DE isoforms.
  • 33 UniProt_IDs were mapped by IPA,and were used for downstream analysis
ipa canonical pathways of de isoforms from f2 vs m2
IPA Canonical Pathways of DE isoforms from F2 vs. M2
  • The y-axis displays the -log of p-value which is calculated by Fisher's exact test right-tailed.
  • The orange points represents Ratio. The ratio is calculated as follows:# of genes in a given pathway that meet cutoff criteria, divided by total # of genes that make up that pathway.
analysis plan a the fast track version4
Analysis Plan A (the fast-track version)
  • Use contigs, counts and homolog (blast result of the contigs) available from the Purdue Genomics Center
  • Differential expression and biomarker discovery
    • Estimate contigs abundance using RSEM
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR:
      • Identify DE contigs/biomarkers among reproduction stages (F1 vs. F2; M1 vs. M2)
      • Identify DE contigs/biomarkers among gender (F1 vs. M1; F2 vs. M2)
    • Functional annotation by Trinotate (a module in the Trinity package).
    • Pathway analysis by Ingenuity Pathway Analysis (IPA).
    • GO term enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery (DAVID)
    • Gene Set Enrichment Analysis (GSEA)
  • Identify proteins in the proteomics data obtained from sturgeon blood of various stages.
  • Comparative genomic study – evolutionary aspect of Sturgeon genome as relates to other fish species
david analysis of de isoforms from f2 vs m2
DAVID analysis of DE isoforms from F2 vs. M2
  • UniProt_ID of DE isoforms from F2_vs_M2 are used for DAVID analysis.
  • There are 46 non-redundant UniProt_IDs from 70 DE isoforms.
  • 41 UniProt_IDs were mapped by IPA,and were used for downstream analysis
analysis plan a the fast track version5
Analysis Plan A (the fast-track version)
  • Use contigs, counts and homolog (blast result of the contigs) available from the Purdue Genomics Center
  • Differential expression and biomarker discovery
    • Estimate contigs abundance using RSEM
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR:
      • Identify DE contigs/biomarkers among reproduction stages (F1 vs. F2; M1 vs. M2)
      • Identify DE contigs/biomarkers among gender (F1 vs. M1; F2 vs. M2)
    • Functional annotation by Trinotate (a module in the Trinity package).
    • Pathway analysis by Ingenuity Pathway Analysis (IPA).
    • GO term enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery (DAVID)
    • Gene Set Enrichment Analysis (GSEA)
  • Identify proteins in the proteomics data obtained from sturgeon blood of various stages.
  • Comparative genomic study – evolutionary aspect of Sturgeon genome as relates to other fish species
gene set enrichment analysis gsea
Gene Set Enrichment Analysis (GSEA)

As a cutoff-free approach, GESA does not require any arbitrary criteria for selecting DE genes/transcripts, and can accumulate subtle expression changes of genes in the gene sets.

sturgeon genes need to be mapped to homologous human genes for gsea analysis
Sturgeon genes need to be mapped to homologous human genes for GSEA analysis
  • To use GSEA to its full capability with non-mammalian model organisms, a microarray platform must be annotated with human gene symbols. Doing so enables the ability to relate a model organism's gene expression, in response to a given treatment, to potential human health consequences of that treatment.
  • Required step: map sturgeon genes to human homologs base on [1].

[1] Thomas MA, Yang L, Carter BJ, Klaper RD. Gene set enrichment analysis of microarray data from Pimephalespromelas (Rafinesque), a non-mammalian model organism. BMC Genomics. 2011 Jan 26;12:66.

analysis plan b the comprehensive version
Analysis Plan B (the comprehensive version)
  • Transcriptome Assembly
    • Identify a couple assemblers to actually run dataset with.
      • Consider the following points when determining the assembler
        • Performance of the assembler on assembling paralogous genes and alternatively spliced genes.
        • Capability of the assembler on multiple k-mer analysis.
      • Among the assemblers mentioned above, Trinity and Oases are available for Galaxy and can be installed through Galaxy tool shed.
    • Evaluate the de novo assemblers, Trinity and Trans-ABySS, using test RNA-Seq datasets, which are selected assembled contigs from Purdue. Reads aligned to the above contigs will be re-assembled using either Trinity or Trans-ABySS, and the results will be compared.
    • Run assemblers on Klaper’s dataset using AWS Galaxy and/or UWM Avi.
    • Align reads to assembled contigs using Bowtie.
    • Compare the LPHIG results with Purdue results.
slide67

Analysis

Sequence assembly

Sequence quality analysis

Abundance estimation

Trinity

FastQC, Trimmomatic

RSEM

Trans-ABySS

Annotation

Differential expression

Sexual Stage Biomarkers

Blast2GO

edgeR

Scaffold contigs

Annotate proteomics data

SSpace

analysis plan b cont
Analysis Plan B (Cont.)
  • Contigs annotation
    • Identify homologous genes: Blast the contigs against NCBI known genes/transcript.
    • Identify GO terms: query the identified homologs against KEGG database using Blast2GO.
  • Differential expression and biomarker discovery
    • Estimate contigs abundance using RSEM
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR, DESeq or EBSeq:
      • Identify DE contigs/biomarkers among reproduction stages (F1 vs. F2; M1 vs. M2)
      • Identify DE contigs/biomarkers among gender (F1 vs. M1; F2 vs. M2)
    • Analyze for GO term enrichment by Gene Set Enrichment Analysis (GSEA)
    • Pathway analysis by Ingenuity Pathway Analysis (IPA).
  • Identify proteins in the proteomics data (obtained from sturgeon blood of various stages) with the above annotated genes using tblastn (search translated nucleotide databases using a protein query).
  • Comparative genomic study – evolutionary aspect of Sturgeon genome as relates to other fish species
sfs genomics center program projects1
SFS Genomics Center Program Projects
  • Project 1: Biomarkers of Reproduction Staging of Sturgeon (Acipenser fulvescens)
  • Project 2: Daphnia Magna Gene Expression under Nanomaterials Exposure
daphnia magna gene expression under nanomaterials exposure
Daphnia Magna Gene Expression under Nanomaterials Exposure

Primary goals:

  • Use RNA-seq to identify the gene expression changes that occur to Daphnia upon acute exposure to nanomaterials of differing surface chemistry, which could potentially be used as biomarkers for exposure and effect.
  • Annotate all the genes/transcripts in the nanomaterial-exposure transcriptome.
  • Identify the genes/transcripts that are common to all treatments as well as unique to each treatment.
analysis plan
Analysis Plan
  • Read alignment against Daphnia pulex
  • Transcriptome de novo assembly by Trinity
  • Contigs annotation by Trinotate
    • Identify homologous genes: Blast the contigs against NCBI known genes/transcript.
    • Identify GO terms: query the identified homologs.
analysis plan1
Analysis Plan
  • Differential expression and biomarker discovery
    • Estimate contigs abundance by RSEM [14].
    • Statistical analysis for significantly differential expressed (DE) contigs using EdgeR
    • Identify DE contigs/biomarkers among groups (Controls vs. D2; Controls vs. NT; Controls vs. NTNH2)
    • Analyze for GO term enrichment by Gene Set Enrichment Analysis (GSEA)
sample description2
Sample Description
  • Raw, unfiltered FASTQ files
quality control
Quality control
  • Quality and adaptor clipped FASTQ files
    • Trimmomatic
quality control1
Quality control
  • Brief quality control review of quality and adaptor clipped sequences using FASTQC does not suggest any serious issues with the data
  • Representative sample:
mapping
Mapping
  • Mapped quality and adaptor clipped reads against Daphnia magna transcriptome assembly
    • Tool: run_RSEM_align_n_estimate.pl (--SS_lib_typeRF, default parameters)
de results
DE results
  • Mapped quality and adaptor clipped reads against Daphnia magna transcriptome assembly
    • Tool: run_DE_analysis.pl (--method edgeR, default parameters)
de results1
DE results

Control verse D2

Control verse D2

P-value ≤ 0.05

FDR ≤ 0.05

1

431

1

377

0

189

478

0

Control verse NT

Control verse NTNH2

Control verse NT

Control verse NTNH2

de results2
DE results
  • Files containing DE results, Blast2GO annotation, and sequence data:
    • 5.30.13.control_verse_D2.txt
    • 5.30.13.control_verse_NT.txt
    • 5.30.13.control_verse_NTNH2.txt
  • Representative sample of the above files:

Blast2GO analysis (from Purdue)

future analysis
Future analysis
  • Annotate using Trinotate (recommended by Trinity developers)
  • Analysis in progress…
future analysis daphnia pulex
Future analysis: Daphnia pulex
  • Comparison of Daphnia magna transcriptome assembly with Daphnia pulextranscriptome
  • Analysis in progress…
future analysis daphnia pulex1
Future analysis: Daphnia pulex
  • Map quality and adaptor clipped reads against Daphnia pulexreference genome
    • Tophat2 (default parameters)
  • Analysis in progress…