Sequenceserver: a modern graphical user interface for custom BLAST databases

← Main Sequenceserver Publication

Supplementary Information

Technical implementation details

We developed Sequenceserver from scratch rather than basing our work on the NCBI’s initial Perl/CGI wwwblast wrapper to reduce technical debt The core of Sequenceserver is written in the Ruby language popular for creating websites and bioinformatics tools while JavaScript and HTML/CSS are used for layout and interactions in the web browser. We use preexisting tools and libraries to facilitate development: The lightweight framework Sinatra is used to create URL endpoints to load the search form and run BLAST searches from the browser. BLAST searches are delegated to the compiled command line version of BLAST we use Ox (https://github.com/ohler55/ox) to parse BLAST XML and create the HTML report. Underscore (http://underscorejs.org/), HTML5 Shiv (https://github.com/afarkas/html5shiv), jQuery (http://jquery.com), jQuery UI (http://jqueryui.com), Webshim (https://afarkas.github.io/webshim/demos), and Bootstrap (http://getbootstrap.com) libraries create a uniform scripting environment (for dynamic aspects of the user interface) and a consistent look-and-feel (for visual layout) across browsers. The d3 (http://d3js.org/) and BioJS libraries are used respectively for generating the graphical overview and the sequence viewing interface. Details regarding versions of the different software libraries are indicated in the source code repository at https://github.com/wurmlab/sequenceserver.

Sustainable software development approach

We followed six software engineering practices to facilitate and accelerate development while increasing robustness, improving the long-term sustainability of the software (). First, we used an open source and agile development approach involving frequent incremental improvements, peer review and frequent deployment on our servers and within the community. Second, we structured the software according to the object-oriented programming paradigm to cleanly separate different parts of code. Third, we followed two important software development principles: “don’t repeat yourself” (DRY) leads to fewer lines of code and thus fewer bugs, and makes it easier to read and understand code than if similar commands are repeated in several places “keep it simple, stupid” (KISS) reduces unnecessary complexity and thus lowers risks and leads to higher maintainability Fourth, we reuse widely established software packages and libraries (see above) to benefit from work done by others. This accelerates our work and reduces the amount of Sequenceserver-specific code, which in turn further reduces the likelihood of adding bugs Fifth, we implemented unit and integration tests for many parts of Sequenceserver’s code, and use continuous integration (https://travis-ci.org/) to ensure these tests are automatically run whenever a change is made to the code, thus increasing the likelihood and speed of detecting errors. Sixth, we use automatic code checkers including rubocop (https://github.com/bbatsov/rubocop) and w3 validator to ensure that our code respects relevant style guides and development principles. Such respect of style standards (e.g., names of variables and methods, code structure and formatting) makes code more accessible to others than if we had chosen no or different conventions Finally, we use the Code Climate platform (http://codeclimate.com) for automated reviews of code quality.

User centric design of graphical user interface

To ensure a fluid user experience that increases researcher productivity, we designed Sequenceserver around eight modern user interface design principles. First, the interface contains only essential information to minimize distractions for the user. Second, the information is laid out in a clear and hierarchically structured manner. As part of this, we paid special attention to typography, using typefaces specifically designed for legibility and aesthetics on electronic devices (Roboto and Open Sans). Third, we used automation where possible to minimize the amount of decisions the user must make. For example, we limit the choices for algorithm selection based on query type and databases selection – this is because only a single basic BLAST algorithm is possible for all cases except for nucleotide-nucleotide search (Figure S1). Fourth, we use interactive visual feedback and cues for step-by-step discovery of the workflow. For example, the BLAST button remains disabled until the user has provided query sequence(s) and selected target databases. If the user tries to click the BLAST button while it is disabled, a tooltip indicates that a required input is missing. Similarly, the selection of protein databases is automatically disabled if the user has already selected a nucleotide database (and vice versa). Fifth, we remain consistent and contextual with regards to user interaction. For example, notification of detection of sequence type does not depend on how the query sequence was provided. This notification is shown below the query sequence input field – where the user is likely to look after query input – instead of using a global designated notification area or displaying pop-up windows that can be disruptive or are ignored. Similarly, a “clear query” button is shown only after the user has provided query sequence(s) and is positioned where a user is likely to look for it. Sixth, we try not to let the advantages of a graphical interface and efforts to create an easily accessible user experience limit the scope of what the user can do. For example, all possible advanced BLAST search options can be entered via a generic input field. Similarly, tooltips over report download links are only shown after the mouse pointer has hovered for at least 500ms. This delay means most users will not be bothered by tooltips after they have used the interface a few times. Seventh, we exploit intuitive human notions of colors. For example, if the user erroneously tries to combine nucleotide and amino acid sequences in the query, the query input-area is gently highlighted using a red border to indicate an error. At a different level, in the graphical overview shown for each query, the color of each hit indicates its strength, with stronger [e-values](/blog/blast-e-value-meaning) being darker. Finally, the wording of error messages is similar to an informal human conversation to create empathy and familiarity, which may also clarify that Sequenceserver is built by a community of scientists.

Supplementary Figure

Table: Research using Sequenceserver

← Main Sequenceserver Publication

Stay up to date

To receive the latest news from our team, enter your email:

Some blog posts you might like: