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https://github.com/scipipe/scipipe

Robust, flexible and resource-efficient pipelines using Go and the commandline
https://github.com/scipipe/scipipe

bioinformatics bioinformatics-pipeline cheminformatics dataflow fbp go golang pipeline scientific-workflows scipipe workflow workflow-engine

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Robust, flexible and resource-efficient pipelines using Go and the commandline

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SciPipe



Robust, flexible and resource-efficient pipelines using Go and the commandline



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Project links: [Documentation & Main Website](http://scipipe.org) | [Issue Tracker](https://github.com/scipipe/scipipe/issues) | [Chat](https://gitter.im/scipipe/scipipe)



## Why SciPipe?



- **Intuitive:** SciPipe works by flowing data through a network of channels

  and processes

- **Flexible:** Wrapped command-line programs can be combined with processes in

  Go

- **Convenient:** Full control over how your files are named

- **Efficient:** Workflows are compiled to binary code that run fast

- **Parallel:** Pipeline paralellism between processes as well as task

  parallelism for multiple inputs, making efficient use of multiple CPU cores

- **Supports streaming:** Stream data between programs to avoid wasting disk space

- **Easy to debug:** Use available Go debugging tools or just `println()`

- **Portable:** Distribute workflows as Go code or as self-contained executable

  files



## Project updates



- Jan 2020: New screencast: "Hello World" scientific workflow in SciPipe

- May 2019: The SciPipe paper published open access in GigaScience: SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines

- Nov 2018: Scientific study using SciPipe: Predicting off-target binding profiles with confidence using Conformal Prediction

- Slides: Presentation on SciPipe and more at Go Stockholm Conference

- Blog post: Provenance reports in Scientific Workflows - going into details about how SciPipe is addressing provenance.

- Blog post: First production workflow run with SciPipe



SciPipe is a library for writing [Scientific

Workflows](https://en.wikipedia.org/wiki/Scientific_workflow_system), sometimes

also called "pipelines", in the [Go programming language](http://golang.org).



When you need to run many commandline programs that depend on each other in

complex ways, SciPipe helps by making the process of running these programs

flexible, robust and reproducible. SciPipe also lets you restart an interrupted

run without over-writing already produced output and produces an audit report

of what was run, among many other things.



SciPipe is built on the proven principles of [Flow-Based Programming](https://en.wikipedia.org/wiki/Flow-based_programming)

(FBP) to achieve maximum flexibility, productivity and agility when designing

workflows.  Compared to plain dataflow, FBP provides the benefits that

processes are fully self-contained, so that a library of re-usable components

can be created, and plugged into new workflows ad-hoc.



Similar to other FBP systems, SciPipe workflows can be likened to a network of

assembly lines in a factory, where items (files) are flowing through a network

of conveyor belts, stopping at different independently running stations

(processes) for processing, as depicted in the picture above.



SciPipe was initially created for problems in bioinformatics and

cheminformatics, but works equally well for any problem involving pipelines of

commandline applications.



**Project status:** SciPipe pretty stable now, and only very minor API changes

might still occur. We have successfully used SciPipe in a handful of both real

and experimental projects, and it has had occasional use outside the research

group as well.



## Known limitations



- There are still a number of missing good-to-have features for workflow

  design. See the [issue tracker](https://github.com/scipipe/scipipe/issues)

  for details.

- There is not (yet) support for the [Common Workflow Language](http://common-workflow-language.github.io).



## Installing



For full installation instructions, see the [intallation page](https://scipipe.org/install/).

For quick getting started steps, you can do:



1. [Download](https://golang.org/dl/) and [install](https://golang.org/doc/install) Go

2. Run the following command, to install the scipipe Go library (don't miss the

   trailing dots!), and create a Go module for your script:



```bash

go install github.com/scipipe/scipipe/...@latest

go mod init myfirstworkflow-module

```



## Hello World example



Let's look at an example workflow to get a feel for what writing workflows in

SciPipe looks like:



```go

package main



import (

    // Import SciPipe, aliased to sp

    sp "github.com/scipipe/scipipe"

)



func main() {

    // Init workflow and max concurrent tasks

    wf := sp.NewWorkflow("hello_world", 4)



    // Initialize processes, and file extensions

    hello := wf.NewProc("hello", "echo 'Hello ' > {o:out|.txt}")

    world := wf.NewProc("world", "echo $(cat {i:in}) World > {o:out|.txt}")



    // Define data flow

    world.In("in").From(hello.Out("out"))



    // Run workflow

    wf.Run()

}

```



To create a file with a similar simple example, you can run:



```

scipipe new hello_world.go

```



## Running the example



Let's put the code in a file named `hello_world.go` and run it.



First you need to make sure that the dependencies (SciPipe in this case) is

installed in your local Go module. This you can do with:



```bash

go mod tidy

```



Then you can go ahead and run the workflow:



```bash

$ go run hello_world.go

AUDIT   2018/07/17 21:42:26 | workflow:hello_world             | Starting workflow (Writing log to log/scipipe-20180717-214226-hello_world.log)

AUDIT   2018/07/17 21:42:26 | hello                            | Executing: echo 'Hello ' > hello.out.txt

AUDIT   2018/07/17 21:42:26 | hello                            | Finished: echo 'Hello ' > hello.out.txt

AUDIT   2018/07/17 21:42:26 | world                            | Executing: echo $(cat ../hello.out.txt) World > hello.out.txt.world.out.txt

AUDIT   2018/07/17 21:42:26 | world                            | Finished: echo $(cat ../hello.out.txt) World > hello.out.txt.world.out.txt

AUDIT   2018/07/17 21:42:26 | workflow:hello_world             | Finished workflow (Log written to log/scipipe-20180717-214226-hello_world.log)

```



Let's check what file SciPipe has generated:



```

$ ls -1 hello*

hello.out.txt

hello.out.txt.audit.json

hello.out.txt.world.out.txt

hello.out.txt.world.out.txt.audit.json

```



As you can see, it has created a file `hello.out.txt`, and `hello.out.world.out.txt`, and

an accompanying `.audit.json` for each of these files.



Now, let's check the output of the final resulting file:



```bash

$ cat hello.out.txt.world.out.txt

Hello World

```



Now we can rejoice that it contains the text "Hello World", exactly as a proper

Hello World example should :)



Now, these were a little long and cumbersome filenames, weren't they? SciPipe

gives you very good control over how to name your files, if you don't want to

rely on the automatic file naming. For example, we could set the first filename

to a static one, and then use the first name as a basis for the file name for

the second process, like so:



```go

package main



import (

    // Import the SciPipe package, aliased to 'sp'

    sp "github.com/scipipe/scipipe"

)



func main() {

    // Init workflow with a name, and max concurrent tasks

    wf := sp.NewWorkflow("hello_world", 4)



    // Initialize processes and set output file paths

    hello := wf.NewProc("hello", "echo 'Hello ' > {o:out}")

    hello.SetOut("out", "hello.txt")



    world := wf.NewProc("world", "echo $(cat {i:in}) World >> {o:out}")

    world.SetOut("out", "{i:in|%.txt}_world.txt")



    // Connect network

    world.In("in").From(hello.Out("out"))



    // Run workflow

    wf.Run()

}

```



In the `{i:in...` part, we are re-using the file path from the file received on

the in-port named 'in', and then running a Bash-style trim-from-end command on

it to remove the `.txt` extension.



Now, if we run this, the file names get a little cleaner:



```bash

$ ls -1 hello*

hello.txt

hello.txt.audit.json

hello_world.go

hello_world.txt

hello_world.txt.audit.json

```



## The audit logs



Finally, we could have a look at one of those audit file created:



```bash

$ cat hello_world.txt.audit.json

{

    "ID": "99i5vxhtd41pmaewc8pr",

    "ProcessName": "world",

    "Command": "echo $(cat hello.txt) World \u003e\u003e hello_world.txt.tmp/hello_world.txt",

    "Params": {},

    "Tags": {},

    "StartTime": "2018-06-15T19:10:37.955602979+02:00",

    "FinishTime": "2018-06-15T19:10:37.959410102+02:00",

    "ExecTimeNS": 3000000,

    "Upstream": {

        "hello.txt": {

            "ID": "w4oeiii9h5j7sckq7aqq",

            "ProcessName": "hello",

            "Command": "echo 'Hello ' \u003e hello.txt.tmp/hello.txt",

            "Params": {},

            "Tags": {},

            "StartTime": "2018-06-15T19:10:37.950032676+02:00",

            "FinishTime": "2018-06-15T19:10:37.95468214+02:00",

            "ExecTimeNS": 4000000,

            "Upstream": {}

        }

    }

```



Each such audit-file contains a hierarchic JSON-representation of the full

workflow path that was executed in order to produce this file. On the first

level is the command that directly produced the corresponding file, and then,

indexed by their filenames, under "Upstream", there is a similar chunk

describing how all of its input files were generated. This process will be

repeated in a recursive way for large workflows, so that, for each file

generated by the workflow, there is always a full, hierarchic, history of all

the commands run - with their associated metadata - to produce that file.



You can find many more examples in the [examples folder](https://github.com/scipipe/scipipe/tree/master/examples) in the GitHub repo.



For more information about how to write workflows using SciPipe, and much more,

see [SciPipe website (scipipe.org)](http://scipipe.org)!



## More material on SciPipe



- See [a poster on SciPipe](http://dx.doi.org/10.13140/RG.2.2.34414.61760), presented at the [e-Science Academy in Lund, on Oct 12-13 2016](essenceofescience.se/event/swedish-e-science-academy-2016-2/).

- See [slides from a recent presentation of SciPipe for use in a Bioinformatics setting](http://www.slideshare.net/SamuelLampa/scipipe-a-lightweight-workflow-library-inspired-by-flowbased-programming).

- The architecture of SciPipe is based on an [flow-based programming](https://en.wikipedia.org/wiki/Flow-based_programming) like

  pattern in pure Go presented in

  [this](http://blog.gopheracademy.com/composable-pipelines-pattern) and

  [this](https://blog.gopheracademy.com/advent-2015/composable-pipelines-improvements/)

  blog posts on Gopher Academy.



## Citing SciPipe



If you use SciPipe in academic or scholarly work, please cite the following paper as source:



Lampa S, Dahlö M, Alvarsson J, Spjuth O. SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines 

_Gigascience_. 8, 5 (2019). DOI: [10.1093/gigascience/giz044](https://dx.doi.org/10.1093/gigascience/giz044)



## Acknowledgements



- SciPipe is very heavily dependent on the proven principles form [Flow-Based

  Programming (FBP)](http://www.jpaulmorrison.com/fbp), as invented by [John Paul Morrison](http://www.jpaulmorrison.com/fbp).

  From Flow-based programming, SciPipe uses the ideas of separate network

  (workflow dependency graph) definition, named in- and out-ports,

  sub-networks/sub-workflows and bounded buffers (already available in Go's

  channels) to make writing workflows as easy as possible.

- This library is has been much influenced/inspired also by the

  [GoFlow](https://github.com/trustmaster/goflow) library by [Vladimir Sibirov](https://github.com/trustmaster/goflow).

- Thanks to [Egon Elbre](http://twitter.com/egonelbre) for helpful input on the

  design of the internals of the pipeline, and processes, which greatly

  simplified the implementation.

- This work is financed by faculty grants and other financing for the [Pharmaceutical Bioinformatics group](http://pharmb.io) of [Dept. of

  Pharmaceutical Biosciences](http://www.farmbio.uu.se) at [Uppsala University](http://www.uu.se), and by [Swedish Research Council](http://vr.se)

  through the Swedish [National Bioinformatics Infrastructure Sweden](http://nbis.se).

- Supervisor for the project is [Ola Spjuth](http://www.farmbio.uu.se/research/researchgroups/pb/olaspjuth).



## Related tools



Find below a few tools that are more or less similar to SciPipe that are worth worth checking out before

deciding on what tool fits you best (in approximate order of similarity to SciPipe):



- [NextFlow](http://nextflow.io)

- [Luigi](https://github.com/spotify/luigi)/[SciLuigi](https://github.com/samuell/sciluigi)

- [BPipe](https://code.google.com/p/bpipe/)

- [SnakeMake](https://bitbucket.org/johanneskoester/snakemake)

- [Cuneiform](https://github.com/joergen7/cuneiform)