Monit: Monitoring your Services

Bioinformatics Applications are moving more in the direction of "Microservices" Architectures where services should be fine-grained and the protocols should be lightweight. Microservices Architectures decomposed the application into different smaller services improving the modularity; making the application easier to develop, deploy and maintain. It also parallelizes development by enabling small autonomous teams to develop, deploy and scale their respective services independently.

With more services (Databases, APIs, Web Applications, Pipelines) more components should be trace, monitor, to know the health of your application. There might be different roles that are played by different services (in different physical/logical machines) that can be even geographically isolated from each other. As a whole, these services/servers might be providing a combined service to the end application. A particular issue or problem on any of the server should not affect the final application behavior and must be found and fixed before the outage happens.

Multiple applications allow developers/devops and sysadmins to monitor all the services in a microservices application, but the most popular ones are Nagios and Monit.

GitHub in Numbers for Bioinformaticians

Impact of Github in Numbers:

An API for all MS-based File formats

We recently released and published our first Java API (Application Programming Interface) for the most common file formats in proteomics, not only ms files but also identification files such as mzIdentML and mztab. 

ms-data-core-api (https://github.com/PRIDE-Utilities/ms-data-core-api)

The library allow the end-users and the developers to use a common data structure for proteomics independently of the file types, and .. But first lets try to understand what is a API.

What is an API?

Imagine you are a builder or civil engineering and your are building your bridge, different components, blocks and different teams needs to be coordinated and plugged for the final results. Wrong communications between the members of the teams, different block sizes or building plans only produced strange results. 

In the simplest terms, APIs are sets of requirements, data structures, objects that govern how applications and software components can talk each other. An API, is a set of routines and protocols that provide building blocks for computer programmers and web developers to build software applications. In the past, APIs were largely associated with computer operating systems and desktop applications. In recent years though, we have seen the emergence of Web APIs (Web Services).

What is ms-data-core-api?

The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. 

The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library.

The future of Proteomics: The Consensus

After the Big Nature papers about the Human Proteome [1][2] the proteomics community has been divided by the same well-known topics than genomics had before: same reasons, same discussions [3-7]. No one discusses about the technical issues, the instrument settings, nothing about the samples processing, even anything about the analytical method (Most of both projects are "common" bottom-up experiments). Main issues are data-analysis problems and still Computational Proteomics Challenges.  

Brazil: A place for Science and Friendship

Búzios

It's really difficult to break stereotypes, especially for developing countries, like Brazil. If you mention its name around the world they are immediately associated with: sports, music, beaches, rum and "País do Carnaval". If you ask to someone in the streets of Germany or China about personalities from Brazil, they will mention Pelé. Breaking stereotypes is a task for years or centuries but we are going in the right direction.

Hotel Ferradura/ Ferradura Resort

Last December I attended to the 2nd Proteomics Meeting of the Brazilian Proteomics Society jointly with the 2nd Pan American HUPO Meeting in Hotel Ferradura/ Ferradura Resort, Búzios, Rio de Janeiro State, Brazil. The venue was gorgeous, mountains close to a small bay that offers calm, clear waters and the open sea. We arrived after 2 hours by car from Rio international airport. My plans, give a talk about PRIDE and ProteomeXchange but more than that, my talk was about "if we really need to share our proteomics data".  

HUPO-PSI Meeting 2014: Rookie’s Notes

Standardisation: the most difficult flower to grow.

The PSI (Proteomics Standard Initiative) 2014 Meeting was held this year in Frankfurt (13-17 of April) and I can say I’m now part of this history. First, I will try to describe with a couple of sentences (for sure I will fai) the incredible venue, the Schloss Reinhartshausen Kempinski. When I saw for the first time the hotel, first thing came to my mind was those films from the 50s. Everything was elegant, classic, sophisticated - from the decoration to a small latch. The food was incredible and the service is first class from the moment you set foot on the front step and throughout the whole stay. 

  

Standardization is the process of developing and implementing technical standards. Standardization can help to maximize compatibility, interoperability, safety, repeatability, or quality. It can also facilitate commoditization of formerly custom processes. In bioinformatics, the standardization of file formats, vocabulary, and resources is a job that all of us appreciate but for several reasons nobody wants to do. First of all, standardization in bioinformatics means that you need to organize and merge different experimental and in-silico pipelines to have a common way to represent the information. In proteomics for example, you can use different sample preparation, combined with different fractionation techniques and different mass spectrometers; and finally using different search engines and post-processing tools. The diversity and possible combinations is needed because allow to explore different solutions for complex problems. (Standarization in Proteomics: From raw data to metadata files).

Analysis of histone modifications with PEAKS 7: A respond to Search Engines comparison from PEAKs Team

Recently we posted a comparison of different search engines for PTMs studies (Evaluation of Proteomic Search Engines for PTMs Identification). After some discussion of the mentioned results in our post the  PEAKS Team just published a blog post with the reanalysis of the dataset. Here the results:

Originally Posted in Peaks Blog:

The complex nature of histone modification patterns has posed as a challenge for bioinformatics analysis over the years. Yuan et al. [1] conducted a study using two datasets from human HeLa histone samples, to benchmark the performance of current proteomic search engines. This article was published in J Proteome Res. 2014 Aug 28 (PubMed), and the data from the two datasets, HCD_Histone and CID_Histone (PXD001118), was made publically available through ProteomeXchange. With this data, the article uses eight different proteomic search engines to compare and evaluate the performance and capability of each. The evaluated search engines in this study are: pFind, Mascot, SEQUEST, ProteinPilot, PEAKS 6, OMSSA, TPP and MaxQuant. 

In this study, PEAKS 6 was used to compare the performance capabilities between search engines. However, PEAKS 7, which was released November 2013, is the latest version available of the PEAKS Studio software. PEAKS 7 not only includes better performance than PEAKS 6, but a lot of additional and improved features. Our team has reanalyzed the two datasets HCD_Histone and CID_Histone with PEAKS 7 to update the ID results presented in the publication by Yuan et al.  These updated results showed that instead, it is PEAKS, pFind and Mascot that identify the most confident results.

Evaluation of Proteomic Search Engines for PTMs Identification

The peptide-centric MS strategy is called bottom-up, in which proteins are extracted from cells, digested into peptides with proteases, and analyzed by liquid chromatography tandem mass spectrometry (LC−MS/MS). More specifically, peptides are resolved by chromatography, ionized in mass spectrometers, and scanned to obtain full MS spectra. Next, some high-abundance peptides (precursor ions) are selected and fragmented to obtain MS/MS spectra by high- energy C-trap dissociation (HCD) or collision-induced dissociation (CID). 

Then, peptides are commonly identified by searching the MS/MS spectra against a database and finally assembled into identified proteins. Database searching plays an important role in proteomics analysis because it can be used to translate thousands of MS/MS spectra into protein identifications (IDs). 

Many database search engines have been developed to quickly and accurately analyze large volumes of proteomics data. Some of the more well-known search engines are Mascot,  SEQUEST, PEAKS DB, ProteinPilot, Andromeda, and X!Tandem. Here a list of commonly use search engines in proteomics and mass spectrometry.

Start a startup or Work for someone else?

Originally posted on P4P:

When you look online for advice about entrepreneurship, you will see a lot of "just do it": 

The best way to get experience... is to start a startup. So, paradoxically, if you're too inexperienced to start a startup, what you should do is start one. That's a way more efficient cure for inexperience than a normal job. - Paul Graham, Why to Not Not Start a Startup

There is very little you will learn in your current job as a {consultant, lawyer, business person, economist, programmer} that will make you better at starting your own startup. Even if you work at someone else’s startup right now, the rate at which you are learning useful things is way lower than if you were just starting your own. -  David Albert, When should you start a startup?

This advice almost never comes with citations to research or quantitative data, from which I have concluded:

The sort of person who jumps in and gives advice to the masses without doing a lot of research first generally believes that you should jump in and do things without doing a lot of research first. 

NEW NIST 2014 mass spectral library

Originally posted in NIST 2014.

Identify your mass spectra with the new NIST 14 Mass Spectral Library and Search Software.

NIST 14 - The successor to NIST 11 (2011) - Is a collection of:

• Electron ionization (EI) mass spectra
• Tandem MS/MS spectra (ion trap and collision cell)
• GC method and retention data
• Chemical structures and names
• Software for searching and identifying your mass spectra
• NIST 14 is integrated with most mass spectral data systems, including Agilent ChemStation/MassHunter, Thermo Xcalibur, and others. The NIST Library is known for its high quality, broad coverage, and accessibility. It is a product of a three decade, comprehensive evaluation and expansion of the world's most widely used and trusted mass spectral reference library compiled by a team of experienced mass spectrometrists in which each spectrum was examined for correctness.

Improvements from 2011 version:

• Increased coverage in all libraries: 32,355 more EI spectra; 138,875 more MS/MS spectra; 37,706 more GC data sets
• Retention index usable in spectral match scoring
• Improved derivative naming, user library features, links to InChIKey, and other metadata.
• Upgrade discount for any previous version
• Lowest Agilent format price available

MS/MS and GC libraries may now be optionally purchased separately at very low cost
Learn what`s new http://www.sisweb.com/software/ms/nist.htm#whatsnew

 Pick related PDFs

Quick Guide to the New Uniprot Web

Probably Uniprot is one of the most used and well-established services in bioinformatics worldwide. With more than 12 years, is one of the major resources of biological information and the reference catalog of protein sequence in the World. The aim of Uniprot is provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information. It started in 2002 when the Swiss‐Prot, TrEMBL and PIR protein database activities have united to form the Universal Protein Knowledgebase (UniProt) consortium.

Making Your Code Citable

Original post from GitHub Guides:

Digital Object Identifiers (DOI) are the backbone of the academic reference and metrics system. If you’re a researcher writing software, this guide will show you how to make the work you share on GitHub citable by archiving one of your GitHub repositories and assigning a DOI with the data archiving tool Zenodo.

ProTip: This tutorial is aimed at researchers who want to cite GitHub repositories in academic literature. Provided you’ve already set up a GitHub repository, this tutorial can be completed without installing any special software. If you haven’t yet created a project on GitHub, start first byuploading your work to a repository.

In the ERA of science communication, Why you need Twitter, Professional Blog and ImpactStory?

Where is the information? Where are the scientifically relevant results? Where are the good ideas? Are these things (only) in journals? I usually prefer to write about bioinformatics and how we should include, annotate and cite our bioinformatics tools inside research papers (The importance of Package Repositories for Science and Research, The problem of in-house tools); but this post represents my take on the future of scientific publications and their dissemination based on the manuscript “Beyond the paper” (1).

In the not too distant future, today’s science journals will be replaced by a set of decentralized, interoperable services that are built on a core infrastructure of open data and evolving standards — like the Internet itself. What the journal did in the past for a single article, the social media and internet resources are doing for the entire scholarly output. We are now immersed in a transition to another science communication system— one that will tap on Web technology to significantly improves dissemination. I prefer to represent the future of science communication by a block diagram where the four main components: (i) Data, (ii) Publications, (iii) Dissemination and (iv) Certification/Reward are completely interconnected:

My List of Most Active Twitter Users in Proteomics

Recently, I published a list of my top influential authors in Computational proteomics. The list was created using a my PhD References and other resources such as linkedin, twitter, google scholar. I will try to do the same here using the most active twitter accounts that i follow. Twitter can be incredibly powerful for both consuming and contributing to the dialogue in your field. Twitter can be an excellent real-time source of new publications, fresh developments, and current opinion.  If you like and use twitter these are some of the twitter account i follow (no order) in Proteomics:

My List of Most Influential Authors in Computational Proteomics (according to Articles References, Google Scholar, twitter, Linkedin, Microsoft Academic Search and ResearchGate)

Young researchers starting their careers will often look for reviews, opinions and research manuscripts from the most influential authors of their chosen field. In science, however, unlike many other topics on the Internet, ranked lists or manuscript repositories of top authors sorted by research topic are hard to come by. For some researchers, the idea of such a task brings the words ‘wasted time’ to their minds; the most critical condemn it as a frivolous pursuit. Maybe so. In my opinion, however, it as an excellent starting point.

Home Page of ResearchGate with more than 3 millions of users

These days, more people than ever are involved in science and research. Just look at ResearchGate’s homepage.  There are over 3 million persons there –and we’re only counting ResearchGate users. Once simple undertakings, such as finding the right manuscript to cite, the most authoritative group on a topic, or the best software application for a specific task, have become increasingly difficult for graduate students navigating this ocean of data, despite the availability of services such as Google Scholar or Pubmed. The situation will only worsen in the future, as is easy to see by simply tallying the number of  published papers in the fields of Proteomics, Genomics, Bioinformatics and Computational Proteomics since 1997:

Number of published manuscripts in Pubmed per year (1997-2012). the statistics was done using the Medline Trend Service http://dan.corlan.net/medline-trend.html

In 2012 alone, over 6,000 and 17,000 manuscripts were published in the fields of proteomics and bioinformatics, respectively. Our young field, computational proteomics, published more than four hundred papers. Perhaps well-established PI’s or Group Leaders can easily tell apart derivative or me-too contributions from groundbreaking work, but young scientists, who spend most of their time implementing someone else’s ideas, can certainly have a hard time doing so. Although technology has come to the rescue with today’s mixture of search engines and social networking tools (ResearchGate, Google Scholar, twitter and LinkedIn among them), the best way to harness its power is, precisely, by starting from a ranked list of the most authoritative voices within a field of research, whose whereabouts can then be traced in the scientific literature, the blogosphere, and anywhere else.

#HavanaBioinfo2012 Hard, but Awesome Experience

From 8th to 11th of last December the "I Bioinformatics for Biotechnology Applications" (#HavanaBioinfo2012) was held in the hotel “Occidental Miramar” located within an elegant area in Havana, Cuba. Putting on a Bioinformatic workshop takes a lot of different pieces (small ones and big ones). You need write lot of mails to invited speakers, you need a nice and comfortable place with lots of tables and chairs and good food and the drinks. 

I started from October, writing the first mails to EBI friends, advisers and professors (www.ebi.ac.uk), and to be complete honest it was fantastic because most if then accepted from the very beginning. Thanks to Henning Hermjakob and Alex Bateman (@Alexbateman1) for the support. At the end the workshop was fully subscribed, with more than 45 attendees and sixteen speakers, participating in two poster sessions and a panel discussion. Speakers from EBI, Belgium University, Mascot (UK), Bioinformatics Solutions (Canada) accepted the invitation to come here and give one or two lectures about proteomics, genomics, bioinformatics.