ESB issueshttps://git-r3lab.uni.lu/groups/ESB/-/issues2023-02-08T10:55:10+01:00https://git-r3lab.uni.lu/ESB/archaea_in_gut/-/issues/1PPC automated checks2023-02-08T10:55:10+01:00Miroslav KratochvilPPC automated checkshttps://git-r3lab.uni.lu/ESB/ensemble_spieceasi/-/issues/1PPC2023-01-11T10:18:01+01:00Laurent Heirendtlaurent.heirendt@uni.luPPChttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/37sars-cov-2 analysis: signal2022-02-14T10:45:59+01:00Valentina Galatavalentina.galata@uni.lusars-cov-2 analysis: signalTry `signal`
- [paper](https://www.mdpi.com/1999-4915/12/8/895)
- [repo](https://github.com/jaleezyy/covid-19-signal)
> This is a complete standardized workflow the assembly and subsequent analysis for short-read viral sequencing.
>
> ...Try `signal`
- [paper](https://www.mdpi.com/1999-4915/12/8/895)
- [repo](https://github.com/jaleezyy/covid-19-signal)
> This is a complete standardized workflow the assembly and subsequent analysis for short-read viral sequencing.
>
> [...]
>
> Briefly, raw reads undergo qc using fastqc (Andrews) before removal of host-related reads [...]
> After this, reads undergo adapter trimming and further qc [...]
> Reads are then mapped to the viral reference [...]
> After this, iVar is used to generate a consensus genome and variants are called [...]
> Coverage statistics are calculated using bedtools before a final QC [...]
> Finally, data from all samples are collated via a post-processing script into an interactive summary for exploration of results and quality control.AnalysisValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/34profiling: agamemnon2022-02-01T10:39:58+01:00Valentina Galatavalentina.galata@uni.luprofiling: agamemnonTry `agamemnon` for profiling of metaG and metaT data.
- [paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4)
- [repo](https://github.com/ivlachos/agamemnon)Try `agamemnon` for profiling of metaG and metaT data.
- [paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4)
- [repo](https://github.com/ivlachos/agamemnon)ProfilingValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/33analysis: alpha-diversity from metaphlan3 results2022-02-01T07:48:44+01:00Valentina Galatavalentina.galata@uni.luanalysis: alpha-diversity from metaphlan3 resultsTo compare alpha-diversity results obtained using different tools and approaches, estimate alpha-diversity from `metaphlan3` output.To compare alpha-diversity results obtained using different tools and approaches, estimate alpha-diversity from `metaphlan3` output.AnalysisValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/32analysis: other options for beta-diversity2022-01-27T13:04:39+01:00Valentina Galatavalentina.galata@uni.luanalysis: other options for beta-diversityTry other options and approaches for beta-diversity
- other input
- other input processing options
- other distance measures
- other projection methodsTry other options and approaches for beta-diversity
- other input
- other input processing options
- other distance measures
- other projection methodsAnalysisValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/30analysis: MerCat (diversity estimation for metaG/metaT)2022-05-13T08:32:00+02:00Valentina Galatavalentina.galata@uni.luanalysis: MerCat (diversity estimation for metaG/metaT)Use `MerCat` for diversity estimation to have support for `mOTUs2`
"MerCat: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from metagenomic and/or metatranscriptomic sequencing data...Use `MerCat` for diversity estimation to have support for `mOTUs2`
"MerCat: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from metagenomic and/or metatranscriptomic sequencing data"
- [paper](https://peerj.com/preprints/2825/)
- [repo](https://github.com/pnnl/mercat)https://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/29analysis: motus2, phyloseq2022-01-24T15:28:35+01:00Valentina Galatavalentina.galata@uni.luanalysis: motus2, phyloseqUse the `motus2` data from `imp3` results for exploratory analysis with `phyloseq`.Use the `motus2` data from `imp3` results for exploratory analysis with `phyloseq`.AnalysisValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/28analysis: ESKAPE pathogens2022-01-07T15:02:19+01:00Valentina Galatavalentina.galata@uni.luanalysis: ESKAPE pathogensLook for the presence of ESKAPE pathogens:
> Bacterial pathogens belonging to the ESKAPE panel consist of five species (Enterococcus faecium,
> Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aerug...Look for the presence of ESKAPE pathogens:
> Bacterial pathogens belonging to the ESKAPE panel consist of five species (Enterococcus faecium,
> Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa)
> and one genus (Enterobacter sp.).
>
> [ref](https://www.biorxiv.org/content/10.1101/2022.01.03.474784v1.full.pdf)AnalysisValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/27profiling: gutsmash2022-01-07T14:50:47+01:00Valentina Galatavalentina.galata@uni.luprofiling: gutsmashGutSMASH:
- [repo](https://github.com/victoriapascal/gutsmash)
- [preprint](https://www.biorxiv.org/content/10.1101/2021.02.25.432841v1)GutSMASH:
- [repo](https://github.com/victoriapascal/gutsmash)
- [preprint](https://www.biorxiv.org/content/10.1101/2021.02.25.432841v1)AnalysisValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/26analysis: classifier2021-12-09T15:43:43+01:00Valentina Galatavalentina.galata@uni.luanalysis: classifierChoose tools to use for classification.Choose tools to use for classification.Analysishttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/25analysis: SARS-CoV-2 coverage2021-11-30T09:00:20+01:00Valentina Galatavalentina.galata@uni.luanalysis: SARS-CoV-2 coverageMap found SARS-CoV-2 reads to a SARS-CoV-2 genome to estimate and plot the coverage.Map found SARS-CoV-2 reads to a SARS-CoV-2 genome to estimate and plot the coverage.Analysishttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/23analysis: maaslin2022-01-24T15:28:35+01:00Susana Martinezanalysis: maaslinTODOs:
* [x] Rscript to run maaslin (from SMA)
* [ ] tmp scripts for `humann3` commands (infer taxonomy, rename gene ids, etc.) (from SMA)
* [ ] required input data
* [ ] Rscript for analysis incl. metadata
MaAsLin2:
- [web site](https:...TODOs:
* [x] Rscript to run maaslin (from SMA)
* [ ] tmp scripts for `humann3` commands (infer taxonomy, rename gene ids, etc.) (from SMA)
* [ ] required input data
* [ ] Rscript for analysis incl. metadata
MaAsLin2:
- [web site](https://huttenhower.sph.harvard.edu/maaslin/)
- [repo](https://github.com/biobakery/Maaslin2)
- [paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009442)AnalysisValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/21profiling: transfer and checks2021-11-18T07:54:44+01:00Valentina Galatavalentina.galata@uni.luprofiling: transfer and checksTransfer the data after the profiling step.Transfer the data after the profiling step.ProfilingValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/18profiling: kaiju2021-10-08T08:46:19+02:00Valentina Galatavalentina.galata@uni.luprofiling: kaijuUse `kaiju` for taxonomic profiling ([repo](https://github.com/bioinformatics-centre/kaiju)).
* [ ] `conda` YAML file
* [ ] rule(s) for databases
* [ ] rule(s) for profiling
* [ ] rule(s) for summaries
* [ ] rule(s) for `krona` input/pl...Use `kaiju` for taxonomic profiling ([repo](https://github.com/bioinformatics-centre/kaiju)).
* [ ] `conda` YAML file
* [ ] rule(s) for databases
* [ ] rule(s) for profiling
* [ ] rule(s) for summaries
* [ ] rule(s) for `krona` input/plots
Input/output: [ref: merge outputs](https://github.com/bioinformatics-centre/kaiju#merging-outputs)
- PE and SE separately for MG/MT, i.e. 4 outputs per sample
- joint output PE+SE for MG/MT, i.e. 2 joint outputs per sample
- see also [kaiju issue 9](https://github.com/bioinformatics-centre/kaiju/issues/9)
- joint output MG+MT from PE+SE, i.e. 1 joint output per sample
Parameters: [ref: accuracy](https://github.com/bioinformatics-centre/kaiju#classification-accuracy)
> For highest sensitivity, it is recommended to use the nr database (+eukaryotes) [...]. Alternatively, use proGenomes over Refseq for increased sensitivity.
>
> Greedy run mode yields a higher sensitivity compared with MEM mode.ProfilingValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/11profiling: kraken2/bracken2021-09-24T14:33:07+02:00Valentina Galatavalentina.galata@uni.luprofiling: kraken2/brackenDatabases: https://benlangmead.github.io/aws-indexes/k2
*TODO*
Bracken: estimating species abundance in metagenomics data
- [web site](https://ccb.jhu.edu/software/bracken/)
- [paper](https://peerj.com/articles/cs-104/)
- [repo](https...Databases: https://benlangmead.github.io/aws-indexes/k2
*TODO*
Bracken: estimating species abundance in metagenomics data
- [web site](https://ccb.jhu.edu/software/bracken/)
- [paper](https://peerj.com/articles/cs-104/)
- [repo](https://github.com/jenniferlu717/Bracken)ProfilingValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/co-infectomics/-/issues/3Dataset of severe cases2021-06-03T10:47:54+02:00Valentina Galatavalentina.galata@uni.luDataset of severe casesCollect samples from severe COVID cases from other studies
- severe cases
- metaG (and metaT) from fecal samplesCollect samples from severe COVID cases from other studies
- severe cases
- metaG (and metaT) from fecal samplesValentina Galatavalentina.galata@uni.luValentina Galatavalentina.galata@uni.luhttps://git-r3lab.uni.lu/ESB/ont_pilot_gitlab/-/issues/126Analysis: metaT cov w.r.t. gene length2021-03-25T08:27:18+01:00Valentina Galatavalentina.galata@uni.luAnalysis: metaT cov w.r.t. gene lengthMetaT coverage of CDS of proteins should be also computed w.r.t. sequence length, i.e. how much of the transcript is covered.MetaT coverage of CDS of proteins should be also computed w.r.t. sequence length, i.e. how much of the transcript is covered.Stretch goalhttps://git-r3lab.uni.lu/ESB/ont_pilot_gitlab/-/issues/122Manuscript: discovery of novel taxa in GDB2021-03-25T08:27:40+01:00Valentina Galatavalentina.galata@uni.luManuscript: discovery of novel taxa in GDBOne of the preprocessing steps was to remove rRNA reads from metaT data using `bbduk`.
However, this step did not remove reads demonstrarting a certain level if dissimilarity to the used references.
Some rRNA genes have a rather high met...One of the preprocessing steps was to remove rRNA reads from metaT data using `bbduk`.
However, this step did not remove reads demonstrarting a certain level if dissimilarity to the used references.
Some rRNA genes have a rather high metaT coverage and that can be used to find "novel taxa", i.e. those not covered by the used rRNA references.Stretch goalhttps://git-r3lab.uni.lu/ESB/ont_pilot_gitlab/-/issues/117Binning - methylation vs non-methylation2021-01-14T08:39:41+01:00Susheel BusiBinning - methylation vs non-methylation- Hypothesis: `methylation status does not affect binning, especially for bacteria`
- To be tested in the future.
- Previous `binning` folders from methylation-aware and non-methylation-aware basecalling stored on the `work` folder for ...- Hypothesis: `methylation status does not affect binning, especially for bacteria`
- To be tested in the future.
- Previous `binning` folders from methylation-aware and non-methylation-aware basecalling stored on the `work` folder for reference and useStretch goalSusheel BusiSusheel Busi