Update documentation and bump version

README.md

@@ -6,7 +6,7 @@
 - [Introduction](#introduction)
 - [Installation](#installation)
 - [Usage](#usage)
-- [Multiple prediction models](#multiple-prediction-models)
+- [Specialized prediction models](#specialized-prediction-models)
 
 ---
 
@@ -34,14 +34,14 @@ If you use MS²PIP for your research, please cite the following articles:
 - Gabriels, R., Martens, L., & Degroeve, S. (2019). Updated MS²PIP web server
 delivers fast and accurate MS² peak intensity prediction for multiple
 fragmentation methods, instruments and labeling techniques. *Nucleic Acids
-Research* https://doi.org/10.1093/nar/gkz299
+Research* [doi:10.1093/nar/gkz299](https://doi.org/10.1093/nar/gkz299)
 - Degroeve, S., Maddelein, D., & Martens, L. (2015). MS²PIP prediction server:
 compute and visualize MS² peak intensity predictions for CID and HCD
 fragmentation. *Nucleic Acids Research*, 43(W1), W326–W330.
-https://doi.org/10.1093/nar/gkv542
+[doi:10.1093/nar/gkv542](https://doi.org/10.1093/nar/gkv542)
 - Degroeve, S., & Martens, L. (2013). MS²PIP: a tool for MS/MS peak intensity
 prediction. *Bioinformatics (Oxford, England)*, 29(24), 3199–203.
-https://doi.org/10.1093/bioinformatics/btt544
+[doi:10.1093/bioinformatics/btt544](https://doi.org/10.1093/bioinformatics/btt544)
 
 Please also take note of and mention the MS²PIP-version you used.
 
@@ -63,7 +63,7 @@ pip install --editable .
 ---
 
 ## Usage
-MS²PIP comes with [pre-trained models](#multiple-prediction-models) for a
+MS²PIP comes with [pre-trained models](#specialized-prediction-models) for a
 variety of fragmentation methods and modifications. These models can easily be
 applied by configuring MS²PIP in the [config file](#config-file) and providing a
 list of peptides in the form of a [PEPREC file](#peprec-file). Optionally,
@@ -93,12 +93,12 @@ optional arguments:
 #### Config file
 Several MS²PIP options need to be set in this config file.
 - `model=X` where X is one of the currently supported MS²PIP models (see 
-[Multiple prediction models](#multiple-prediction-models)).
+[Specialized prediction models](#specialized-prediction-models)).
 - `frag_error=X` where is X is the fragmentation spectrum mass tolerance in Da
 (only relevant if an MGF file is passed).
 - `out=X` where X is a comma-separated list of a selection of the currently
-supported output file formats: `csv`, `mgf`, `msp`, or `bibliospec` (SSL/MS2,
-also for Skyline). For example: `out=csv,msp`.
+supported output file formats: `csv`, `mgf`, `msp`, `spectronaut`, or 
+`bibliospec` (SSL/MS2, also for Skyline). For example: `out=csv,msp`.
 - `ptm=X,Y,opt,Z` for every peptide modification where:
   - `X` is a string that represents the 
 PTM name (needs to match the names in the [PEPREC file](#peprec-file)).
@@ -152,7 +152,7 @@ ptm=Carbamidomethyl,57.02146,opt,C
 ptm=Acetyl,42.010565,opt,N-term
 ptm=Glyloss,-58.005479,opt,C-term
 ```
-then the PEPREC file could look like this:
+then the **PEPREC file** could look like this:
 ```
 spec_id modifications peptide charge
 peptide1 - ACDEK 2
@@ -176,15 +176,16 @@ END IONS
 ```
 
 ### Output
-The predictions are saved in a `.csv` file with the name
-`<peptide_file>_predictions.csv`.
-If you want the output to be in the form of an `.mgf` file, replace the
-variable `mgf` in line 716 of `ms2pipC.py`.
+The predictions are saved in the output file(s) specified in the
+[config file](#config-file). Note that the normalization of intensities depends
+on the output file format. In the CSV file output, intensities are
+log2-transformed. To "unlog" the intensities, use the following formula:
+`intensity = (2 ** log2_intensity) - 0.001`.
 
 ---
 
-## Multiple prediction models
-MS²PIP contains multiple specific prediction models, fit for peptide spectra
+## Specialized prediction models
+MS²PIP contains multiple specialized prediction models, fit for peptide spectra
 with different properties. These properties include fragmentation method,
 instrument, labeling techniques and modifications. As all of these properties
 can influence fragmentation patterns, it is important to match the MS²PIP model
@@ -196,27 +197,29 @@ models also include predictions for doubly charged fragment ions (b++ and y++),
 next to the predictions for singly charged b- and y-ions. 
 
 ### MS² acquisition information and peptide properties of the models' training datasets
-Model | Fragmentation method | MS² mass analyzer | Peptide properties
--|-|-|-
-HCD | HCD | Orbitrap | Tryptic digest
-CID | CID | Linear ion trap | Tryptic digest
-iTRAQ | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled
-iTRAQphospho | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled, enriched for phosphorylation
-TMT | HCD | Orbitrap | Tryptic digest, TMT-labeled
-TTOF5600 | CID | Quadrupole Time-of-Flight | Tryptic digest
-HCDch2 | HCD | Orbitrap | Tryptic digest
-CIDch2 | CID | Linear ion trap | Tryptic digest
+
+| Model | Fragmentation method | MS² mass analyzer | Peptide properties |
+| - | - | - | - |
+| HCD | HCD | Orbitrap | Tryptic digest |
+| CID | CID | Linear ion trap | Tryptic digest |
+| iTRAQ | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled |
+| iTRAQphospho | HCD | Orbitrap | Tryptic digest, iTRAQ-labeled, enriched for phosphorylation |
+| TMT | HCD | Orbitrap | Tryptic digest, TMT-labeled |
+| TTOF5600 | CID | Quadrupole Time-of-Flight | Tryptic digest |
+| HCDch2 | HCD | Orbitrap | Tryptic digest |
+| CIDch2 | CID | Linear ion trap | Tryptic digest |
 
 ### Models, version numbers, and the train and test datasets used to create each model
-Model | Current version | Train-test dataset (unique peptides) | Evaluation dataset (unique peptides) | Median Pearson correlation on evaluation dataset
--|-|-|-|-
-HCD | v20190107 | [MassIVE-KB](https://doi.org/10.1016/j.cels.2018.08.004) (1 623 712) | [PXD008034](https://doi.org/10.1016/j.jprot.2017.12.006) (35 269) | 0.903786
-CID | v20190107 | [NIST CID Human](https://chemdata.nist.gov/) (340 356) | [NIST CID Yeast](https://chemdata.nist.gov/) (92 609) | 0.904947
-iTRAQ | v20190107 | [NIST iTRAQ](https://chemdata.nist.gov/) (704 041) | [PXD001189](https://doi.org/10.1182/blood-2016-05-714048) (41 502) | 0.905870
-iTRAQphospho | v20190107 | [NIST iTRAQ phospho](https://chemdata.nist.gov/) (183 383) | [PXD001189](https://doi.org/10.1182/blood-2016-05-714048) (9 088) | 0.843898
-TMT | v20190107 | [Peng Lab TMT Spectral Library](https://doi.org/10.1021/acs.jproteome.8b00594) (1 185 547) | [PXD009495](https://doi.org/10.15252/msb.20188242) (36 137) | 0.950460
-TTOF5600 | v20190107 | [PXD000954](https://doi.org/10.1038/sdata.2014.31) (215 713) | [PXD001587](https://doi.org/10.1038/nmeth.3255) (15 111) | 0.746823
-HCDch2 | v20190107 | [MassIVE-KB](https://doi.org/10.1016/j.cels.2018.08.004) (1 623 712) | [PXD008034](https://doi.org/10.1016/j.jprot.2017.12.006) (35 269) | 0.903786 (+) and 0.644162 (++)
-CIDch2 | v20190107 | [NIST CID Human](https://chemdata.nist.gov/) (340 356) | [NIST CID Yeast](https://chemdata.nist.gov/) (92 609) | 0.904947 (+) and 0.813342 (++)
+
+| Model | Current version | Train-test dataset (unique peptides) | Evaluation dataset (unique peptides) | Median Pearson correlation on evaluation dataset |
+| - | - | - | - | - |
+| HCD | v20190107 | [MassIVE-KB](https://doi.org/10.1016/j.cels.2018.08.004) (1 623 712) | [PXD008034](https://doi.org/10.1016/j.jprot.2017.12.006) (35 269) | 0.903786 |
+| CID | v20190107 | [NIST CID Human](https://chemdata.nist.gov/) (340 356) | [NIST CID Yeast](https://chemdata.nist.gov/) (92 609) | 0.904947 |
+| iTRAQ | v20190107 | [NIST iTRAQ](https://chemdata.nist.gov/) (704 041) | [PXD001189](https://doi.org/10.1182/blood-2016-05-714048) (41 502) | 0.905870 |
+| iTRAQphospho | v20190107 | [NIST iTRAQ phospho](https://chemdata.nist.gov/) (183 383) | [PXD001189](https://doi.org/10.1182/blood-2016-05-714048) (9 088) | 0.843898 |
+| TMT | v20190107 | [Peng Lab TMT Spectral Library](https://doi.org/10.1021/acs.jproteome.8b00594) (1 185 547) | [PXD009495](https://doi.org/10.15252/msb.20188242) (36 137) | 0.950460 |
+| TTOF5600 | v20190107 | [PXD000954](https://doi.org/10.1038/sdata.2014.31) (215 713) | [PXD001587](https://doi.org/10.1038/nmeth.3255) (15 111) | 0.746823 |
+| HCDch2 | v20190107 | [MassIVE-KB](https://doi.org/10.1016/j.cels.2018.08.004) (1 623 712) | [PXD008034](https://doi.org/10.1016/j.jprot.2017.12.006) (35 269) | 0.903786 (+) and 0.644162 (++) |
+| CIDch2 | v20190107 | [NIST CID Human](https://chemdata.nist.gov/) (340 356) | [NIST CID Yeast](https://chemdata.nist.gov/) (92 609) | 0.904947 (+) and 0.813342 (++) |
 
 To train custom MS²PIP models, please refer to [Training new MS²PIP models](http://compomics.github.io/projects/ms2pip_c/wiki/Training-new-MS2PIP-models.html) on our Wiki pages.

setup.py

@@ -24,12 +24,12 @@
 
 setup(
 	name='ms2pip',
-	version='20190805',
+	version='v20191029',
 	description='MS²PIP: MS² Peak Intensity Prediction',
 	author='Sven Degroeve, Ralf Gabriels, Ana Sílvia C. Silva',
 	author_email='sven.degroeve@vib-ugent.be',
 	url='https://www.github.com/compomics/ms2pip_c',
-	packages=['ms2pip', 'ms2pip.ms2pip_tools'],
+	packages=['ms2pip', 'ms2pip.ms2pip_tools', 'fasta2speclib'],
 	include_package_data=False,
 	entry_points={
 		'console_scripts': [