Merge pull request #51 from nf-core/dev

Patch release v1.1.1

.github/workflows/ci.yml

@@ -22,7 +22,7 @@ jobs:
       - name: Pull docker image
         run: |
           docker pull nfcore/coproid:dev
-          docker tag nfcore/coproid:dev nfcore/coproid:1.1
+          docker tag nfcore/coproid:dev nfcore/coproid:1.1.1
       - name: Run pipeline with test data
         run: |
           nextflow run ${GITHUB_WORKSPACE} -profile test,docker

.github/workflows/linting.yml

@@ -12,9 +12,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
-      - uses: actions/setup-node@v1
-        with:
-          node-version: '10'
+      - uses: actions/setup-node@v2
+
       - name: Install markdownlint
         run: npm install -g markdownlint-cli
       - name: Run Markdownlint
@@ -45,6 +44,6 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install nf-core
+          pip install nf-core==1.9
       - name: Run nf-core lint
         run: nf-core lint ${GITHUB_WORKSPACE}

.nf-core-lint.yml

@@ -0,0 +1,2 @@
+files_unchanged:
+  - Dockerfile

CHANGELOG.md

@@ -1,5 +1,11 @@
 # nf-core/coproid: Changelog
 
+## v.1.1.1
+
+### Fixed
+
+- Fix [#37](https://github.com/nf-core/coproid/issues/37) with conda environment and docker container update
+
 ## v1.1
 
 - Update mapped basepair count to be quicker and include it in report [#14](https://github.com/nf-core/coproid/pull/14)
@@ -8,7 +14,7 @@
 - Update Sourcepredict to version 0.4 and reflect new parameters in coproID [#19](https://github.com/nf-core/coproid/pull/19) [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) [
 c2d4164](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d529470b0a3af3a69113a)
 - Changed bedtools bamtofastq to samtools fastq [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8)
-- Fixed column names in report (PC* to DIM* ) [e4afca7](https://github.com/nf-core/coproid/commit/63a6bc6998c240b77791916c243d538b2268b5d5)
+- Fixed column names in report (`PC*` to `DIM*`) [e4afca7](https://github.com/nf-core/coproid/commit/63a6bc6998c240b77791916c243d538b2268b5d5)
 - Update README to inlude Zenodo badge, Quick start, contributor section, and tools references. [9874ae8](https://github.com/nf-core/coproid/commit/9874ae87c88842d75c29088672aa81023408d4e7) [e85988b](https://github.com/nf-core/coproid/commit/e85988b883539aa51461e749bc14ec6563f62fc8)
 - Update documentation [bedfdde](https://github.com/nf-core/coproid/commit/bedfddec8500adac8e0cb9cc8e0df2dc6a784f15)
 - Update Nextflow minimum version to 19.04.0 [44999fd](https://github.com/nf-core/coproid/commit/44999fd4d38b21d53f970621dbf3587c044da8d1)

Dockerfile

@@ -1,15 +1,12 @@
-FROM nfcore/base:1.9
+FROM condaforge/mambaforge
 LABEL authors="Maxime Borry" \
     description="Docker image containing all software requirements for the nf-core/coproid pipeline"
 
 # Install the conda environment
 COPY environment.yml /
-RUN conda env create -f /environment.yml && conda clean -a
-RUN conda env export --name nf-core-coproid-1.1 > nf-core-coproid-1.1.yml
-ENV PATH /opt/conda/envs/nf-core-coproid-1.1/bin:$PATH
-
-# Dump the details of the installed packages to a file for posterity
-RUN conda env export --name nf-core-coproid-1.1 > nf-core-coproid-1.1.yml
+RUN mamba env create -f /environment.yml && mamba clean -a
+RUN mamba env export --name nf-core-coproid-1.1.1 > nf-core-coproid-1.1.1.yml
+ENV PATH /opt/conda/envs/nf-core-coproid-1.1.1/bin:$PATH
 
 # Numba cache dir patch
 ENV NUMBA_CACHE_DIR /tmp

README.md

@@ -17,8 +17,8 @@
 
 It combines the analysis of putative host ancient DNA with a machine learning prediction of the feces source based on microbiome taxonomic composition:
 
-- (**A**) First coproID performs a comparative mapping of all reads agains two (or three) target genomes (genome1, genome2, and eventually genome3) and computes a host-DNA species ratio (*NormalizedRatio*)
-- (**B**) Then coproID performs a metagenomic taxonomic profiling, and compares the obtained profiles to modern reference samples of the target species metagenomes. Using [machine learning](https://joss.theoj.org/papers/10.21105/joss.01540), coproID then estimates the host source from the metagenomic taxonomic composition (*prop_microbiome*).
+- (**A**) First coproID performs a comparative mapping of all reads agains two (or three) target genomes (genome1, genome2, and eventually genome3) and computes a host-DNA species ratio (_NormalizedRatio_)
+- (**B**) Then coproID performs a metagenomic taxonomic profiling, and compares the obtained profiles to modern reference samples of the target species metagenomes. Using [machine learning](https://joss.theoj.org/papers/10.21105/joss.01540), coproID then estimates the host source from the metagenomic taxonomic composition (_prop_microbiome_).
 - Finally, coproID combines **A** and **B** to predict the likely host of the metagenomic sample.
 
 The coproID pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.