Scientists from the University of Oslo, Norway, presented a brand-new Python module called BioNumPy that builds a layer on top of NumPy to make it possible to program intuitively with arrays on biological datasets. The goal of BioNumPy is to make it simple to read popular bioinformatics file formats into NumPy-like data structures for quick operations and data processing.
Python is a well-known and often used programming language for scientific computing, in significant part because of the powerful array programming module NumPy, which makes it easy to build clean, vectorized, and efficient code for handling large datasets. The fact that biological data is frequently non-numeric and changeable in length (such as DNA sequences) makes it difficult to employ conventional array programming approaches out of the box. In order to build effective code, bioinformatics has a history of using low-level languages like C and C++. Because of this, the tools are less transparent to the typical computational biologist, which makes them more challenging to comprehend, alter, and contribute to.
The Python programming language now has functionality for massive, multi-faceted arrays and matrices, as well as a large number of high-level mathematical functions to work on these arrays, thanks to the NumPy package.
A Python package called BioNumPy, which is based on NumPy, enables array programming on biological datasets in Python. BioNumPy is able to quickly load biological datasets (such as FASTQ files, BED files, and BAM files) into data structures that resemble NumPy operations so that the data may be subjected to manipulations like indexing, vectorized functions and reductions. It has been demonstrated that for common bioinformatics tasks, BioNumPy is significantly quicker than vanilla Python and other Python packages and, in many cases, is on par with tools created in C/C++. Thus, BioNumPy fills in a long-standing vacuum in bioinformatics by enabling the use of a similar programming language (Python) throughout the whole range, from short and easy scripts to computationally effective processing of massive amounts of data.
One of the most popular and rapidly expanding programming languages is Python. Given its high level, Python is adaptable and suitable for a wide range of studies. For biologists who are new to programming, it is simple to learn, and for established bioinformaticians, it is a powerful language. But the typical problem is that plain old Python needs to be more active to be a practical choice for extensive studies. As a result, bioinformaticians frequently wind up employing opaque and error-prone UNIX command line one-liners or creating and utilizing tools designed in low-level languages like C and C++.
Python is widely and effectively used for revved-up enumerations and large-scale analysis in different scientific domains (such as physics, engineering, and machine learning). This is primarily because of the highly potent array programming toolkit NumPy, which makes it possible to represent and analyze numerical data quickly and efficiently in memory (similar to R and MATLAB).
BioNumPy: a prominent biological library
A Python module called BioNumPy makes it simple to read, represent, and analyze biological information. Because NumPy is used for all time-critical operations, BioNumPy has performance that is on par with specially designed low-level language implementations.
The main characteristics of BioNumPy are:
- Reading and writing biological datasets directly to and from data structures resembling those in NumPy enabling simple access to the data via a user-friendly API.
- Implementing a NumPy-like user interface to process and analyze such biological data effectively.
BioNumPy as a potential BioPython tool
There is also a component of the Python documentation geared for scientists. To make Python more amenable to science, several biology programmers have already donated libraries. A brand-new Python module called BioNumPy has been introduced for the effective modeling and study of biological datasets. BioNumPy is typically much quicker than both vanilla Python scripts and frequently used Python libraries. Additionally, BioNumPy’s efficiency is equivalent to that of widely known C/C++-based efficient tools.
While generating reverse complements of reads and counting kmers are fundamental operations that BioNumPy is quick at, the emphasis is that BioNumPy is not specially made for standard jobs where specialized and highly optimized tools already exist. Instead, BioNumPy is designed to be used as a library inside Python and is helpful
when someone wishes to carry out several operations on a dataset, investigate or play around with datasets, or carry out studies that creatively combine various datasets. This study also allows the community to provide a wide range of specialized features using BioNumPy as the main plodder.
BioNumPy is not necessarily quicker than plain Python code, for example, when reading a FASTA file, subsampling the sequences, and returning the results back to the file. The explanation behind this is that while BioNumPy receives all data into NumPy-arrays that can be successfully subsampled, it also performs actions that go above and beyond what is provided by the standard Python version, such as verifying, encoding and efficiently displaying the data. These stages show their potential significance when multi-operational datasets are in function, such as integrating it with other datasets or querying it in other ways, these extra stages are helpful.
Computing the Jaccard similarity index between all pairings of a collection of bed files is one scenario where BioNumPy outperforms native tools by a wide margin. BioNumPy is significantly quicker than specialized programs like BEDTools because it can maintain all files in memory rather than requiring each bed file to be read from disk each time two BED files are to be evaluated.
Due to the flexibility of BioNumPy’s input, its framework is designed to help with other programs and methods for downloading data from databases, such as the many BioPython modules for downloading data from databases like Encode and Jaspar. Because of the simplicity of communicating information in a coordinated manner, the scope of BioNumPy is restricted to exclude modules for things like retrieving online databases.
Many major bioinformatics tools are instead built-in low-level, more difficult-to-learn languages like C or C++, which may be due to the difficulties of generating clean code for humongous analysis in Python. The vast majority of bioinformaticians and computational biologists, who generally only know bash, R, and/or Python, are unable to readily assist in the creation of tools or understand/learn the internal dynamics of the methodologies they employ since tools are built such languages. This has an impact on the accessibility and credibility of bioinformatics research and is a larger issue since tools and libraries must be quick and effective due to the expanding volume of biological data. By enabling anybody to interact with huge biological datasets in Python more readily, BioNumPy is intended to close this gap.
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Riya Vishwakarma is a consulting content writing intern at CBIRT. Currently, she's pursuing a Master's in Biotechnology from Govt. VYT PG Autonomous College, Chhattisgarh. With a steep inclination towards research, she is techno-savvy with a sound interest in content writing and digital handling. She has dedicated three years as a writer and gained experience in literary writing as well as counting many such years ahead.