[Nancy Zhu](/content/authors/nancy-zhu/index.html)

March 15, 2021 \| 5

## Open source bioinformatics tools

Bioinformatics is the application of informatics techniques to acquire, store
and analyze large and complex biological data. With the rise of data science in
every discipline nowadays, bioinformatics can be seen as the data science of
biology.

Genomes and protein sequences are the most common data in
bioinformatics. Many of the open source tools commonly used in data science have
extended their capability to include analysis of bioinformatic data such as these.

### Open source bioinformatics tools

#### BLAST

BLAST (Basic Local Alignment Search Tool) is a set of algorithms and programs
for comparing nucleotide or amino acid sequences to sequence databases. It is an
excellent tool to study unknown sequences. By aligning the unknown sequences
with existing databases, it helps to uncover their functions.

There are two main ways to access BLAST: web interface and standalone. Both are
available from the [U.S. National Center for Biotechnology Information (NCBI)](https://blast.ncbi.nlm.nih.gov/Blast.cgi).
The web interface is user friendly. However, if you need to run multiple
sequence alignments and keep your search confidential, standalone BLAST is very
much preferred.

To run BLAST locally, both BLAST+ and reference databases need to be
downloaded and installed. BLAST can be run with the command line.

##### Example BLAST search from the command line

This example shows a BLAST search of FASTA sequence file `query.fsa` against the
`nt` database, saving the result to `result.csv` file with selected columns
`sseqie`, `qstart`, `qend` and `evalue`.

```shell
blastn \
  -db nt \
  -query query.fsa \
  -outfmt "10 sseqid qstart qend evalue" \
  -out results.csv
```

The resulting CSV file can be loaded into other software such as R or Python for
any downstream analysis.

Even with the advent of advanced bioinformatic techniques, BLAST is still one of
the most used approaches in biology today. To learn more about running BLAST,
you can consult [the online manual by NCBI](https://www.ncbi.nlm.nih.gov/books/NBK279690/).

#### R Bioconductor

The [statistical programming language R](https://cran.r-project.org/)
has long been used in academic environments for data analysis and visualization. It is not surprising that R
was picked by the biology community to solve many of their data problems.

[_Bioconductor_](https://www.bioconductor.org/) is an open source and open development software development
project that uses R for computational biology and bioinformatics. Established in
2001, most of the packages in _Bioconductor_ were initially developed to analyze
high throughput genomic data. Since then, the functional scope of the packages
has been broadened to include analysis of next generation sequencing (NGS) data,
flow cytometry data and 3D protein structures.

To [install any packages from _Bioconductor_](https://www.bioconductor.org/install/), `BioManager` needs to be installed
first:

```r
install.packages("BiocManager")

# Install Bioconductor package(s)
BiocManager::install("Biostrings")
```

##### A short example using _Bioconductor_ for pairwise alignment of biological strings

```r
# Use pairwise alignment to look for difference in two amino acid sequences
# seq1: SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQA
#   - human coronavirus 3C like protease (partial)
# seq2: SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDTVYCPRHVICTAEDMLNPNYEDLLIRKSNHSFLVQA
#   - SARS 3CLpro C145A mutant (partial)
library(Biostrings)
aa_1 <- AAString(
  "SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQA"
)
aa_2 <- AAString(
  "SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDTVYCPRHVICTAEDMLNPNYEDLLIRKSNHSFLVQA"
)
align <- pairwiseAlignment(aa_1, aa_2)
mismatchTable(align)
```

```r
##   PatternId PatternStart PatternEnd PatternSubstring PatternQuality
## 1         1           35         35                V              7
## 2         1           46         46                S              7
## 3         1           65         65                N              7
##   SubjectStart SubjectEnd SubjectSubstring SubjectQuality
## 1           35         35                T              7
## 2           46         46                A              7
## 3           65         65                S              7
```

The [_Biostrings_ package](https://bioconductor.org/packages/release/bioc/html/Biostrings.html)
is a one stop shop for DNA, RNA or amino acid sequence
manipulation. The example above is a pairwise alignment between coronavirus
3C-like protease (partial) and a mutant of it. From the summary table, we know
there are 3 mutations at location 35, 46 and 65 in the sequences.

Currently, there are 1974 software packages in _Bioconductor_, covering topics
from basic biological data manipulation to machine learning frameworks. A
complete list of packages can be found [on the _Bioconductor_ website](https://www.bioconductor.org/packages/release/BiocViews.html#___Software).

#### Python Biopython

In recent years, Python has surpassed R to become the most commonly used
programming language in data science. As the number of Python programmers
increases, there is also a growth in the number of Python tools for
bioinformatics.

[_Biopython_](https://biopython.org/) is an open source collection of Python tools that includes
functions to load and characterize genomic and protein sequence data.

The installation is rather straightforward:

```shell
pip install biopython
```

##### A short example using _Biopython_ for pairwise alignment (same sequences as previous example)

```python
from Bio import pairwise2
from Bio.Seq import Seq
seq1 = Seq("SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQA")
seq2 = Seq("SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDTVYCPRHVICTAEDMLNPNYEDLLIRKSNHSFLVQA")

alignments = pairwise2.align.globalxx(seq1, seq2)

print(pairwise2.format_alignment(*alignments[0]))
```

```python
## SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDV-VYCPRHVICTS-EDMLNPNYEDLLIRKSNHN-FLVQA
## ||||||||||||||||||||||||||||||||||  ||||||||||  ||||||||||||||||||  |||||
## SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDD-TVYCPRHVICT-AEDMLNPNYEDLLIRKSNH-SFLVQA
##   Score=67
```

### Which tool to use?

As a single package, _Biopython_ is very useful for sequence data manipulation
and basic machine learning techniques such as clustering. Nevertheless,
_Bioconductor_ still provides a wider selection of tools for
bioinformatics, especially for statistical analysis. That being said, I am not
starting another discussion on R vs Python. Each of them has pros and cons.
In the end, the choice really depends on your project requirements.

There are also many other open source tools for bioinformatics including Orange and
the Perl programming language. To keep the overview brief, only 3 were discussed
here. With these open source tools at your fingertips, you can start today on
your bioinformatics journey!

#### Interested in learning about R?

Find out more about our R training courses by contacting us at
[contact@precision-analytics.ca](mailto:contact@precision-analytics.ca)!

### [Nancy Zhu](/content/authors/nancy-zhu/index.html)

I am a Masters student in Epidemiology at McGill University. In summer 2017, I
did an internship at Health Canada, where I developed …

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