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Prepare to release optimus v1 (#177)
* Prepare to release Optimus v1 * Add changelog.md * Finish the skeleton of the changelog. * Remove the pointer to the google docs for Optimus. * Update README.txt * Add diagram for Optimus. * Address comments. * Add the pointer to the acceptance report.
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# Skylab Change Log | ||
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## Optimus | ||
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### Optimus_v1.0.0 | ||
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- The first major version release for the Optimus pipeline. | ||
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## Smart-seq2 | ||
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### smartseq2_v2.2.0 | ||
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### smartseq2_v2.1.0 | ||
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### smartseq2_v2.0.0 | ||
- Add SS2 group qc metrics and zarr output files. | ||
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### smartseq2_v1.0.0 | ||
- The first major version release for the Smart-seq2 pipeline. | ||
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## Cellranger | ||
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### cellranger_v1.0.2 | ||
- Add max_retries runtime parameter. | ||
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### cellranger_v1.0.1 | ||
- Increase disk space. | ||
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### cellranger_v1.0.0 | ||
- The first major version release for the Cellranger pipeline. |
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# skylab | ||
Secondary analysis pipelines for the Human Cell Atlas. | ||
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[![Snyk Vulnerabilities for GitHub Repo (Specific Manifest)](https://img.shields.io/snyk/vulnerabilities/github/HumanCellAtlas/skylab/docker/cellranger/requirements.txt.svg?label=Snyk%20Scripts%20Vulnerabilities&logo=Snyk)](https://snyk.io/test/github/HumanCellAtlas/skylab?targetFile=docker/cellranger/requirements.txt) | ||
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[![GitHub Release](https://img.shields.io/github/release-pre/HumanCellAtlas/skylab.svg?label=Latest%20Release&style=flat-square&colorB=green)](https://github.com/HumanCellAtlas/skylab/releases) | ||
[![License](https://img.shields.io/github/license/HumanCellAtlas/skylab.svg?style=flat-square)](https://github.com/HumanCellAtlas/skylab/blob/master/LICENSE) | ||
[![Snyk Vulnerabilities for GitHub Repo (Specific Manifest)](https://img.shields.io/snyk/vulnerabilities/github/HumanCellAtlas/skylab/docker/cellranger/requirements.txt.svg?style=flat-square&label=Snyk%20Scripts%20Vulnerabilities&logo=Snyk)](https://snyk.io/test/github/HumanCellAtlas/skylab?targetFile=docker/cellranger/requirements.txt) | ||
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## Pipelines | ||
- [cellranger](https://github.com/HumanCellAtlas/skylab/tree/master/pipelines/cellranger) secondary analysis pipeline | ||
- [Optimus](https://github.com/HumanCellAtlas/skylab/tree/master/pipelines/optimus) secondary analysis pipeline | ||
- [Smart-seq2](https://github.com/HumanCellAtlas/skylab/tree/master/pipelines/smartseq2_single_sample) secondary analysis pipeline | ||
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## How to run pipelines from skylab | ||
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Tasks on Cromwell may be run on what are known as "preemptible" machines to reduce costs by a significant amount. The catch with preemptible machines is that they may be "preempted" at any given moment--as in, google may shut down the task to re-use the resources. | ||
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Many tasks are set to automatically be `preemptible = 3`, aka they will be run on preemptible instances for up to 3 instances of preemption, after which it will be run on a non-preemptible machine. The Optimus pipeline in particular also has a `preemptible` input that overrides the default preemptible parameter on all tasks run through optimus. This option may be set to 0 to run the entire workflow without using preemptible machines. | ||
Many tasks are set to automatically be `preemptible = 3`, aka they will be run on preemptible instances for up to 3 instances of preemption, after which it will be run on a non-preemptible machine. This option may be set to 0 by passing a task-level input to the workflow (i.e. Optimus.StarAlign.preemptible), causing the task to be run without using preemptible machines. This option is especially useful for long-running tasks, which have a chance to take a very long time to run due to being preempted multiple times. | ||
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### maxRetries | ||
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# Optimus Pipeline | ||
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![Diagram](Optimus_diagram.png) | ||
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The Optimus pipeline is a pipeline for processing 3' single-cell expression data generated with the 10X Genomic V2 assay, developed by the Data Coordination Platform (DCP) of the Human Cell Atlas (HCA) Project. | ||
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Optimus is a alignment and transcriptome quantification pipeline. Optimus corrects Cell Barcodes (CBs) and Unique Molecular Identifiers (UMIs), aligns reads to the genome, generates an expression count matrix in a UMI-aware manner, detects empty droplets, calculates summary statistics for genes and cells, and returns outputs in BAM and Zarr file formats. Special care is taken to keep all reads that may be useful to the downstream user, such as unaligned reads or reads with uncorrectable barcodes. This design provides flexibility to the downstream user and allows for alternative filtering or leveraging the data for novel methodological development. | ||
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More details can be found in the [Optimus Acceptance Report](https://docs.google.com/document/d/158ba_xQM9AYyu8VcLWsIvSoEYps6PQhgddTr9H0BFmY/edit) |