Connect with us

Bioinformatics Programming

Nested SQL queries and aggregate functions for complex information retrieval from a database

Dr. Muniba Faiza

Published

on

SQL queries for complex information retrieval

In one of our previous articles, we have mentioned a few basic SQL queries to search in a database. In this article, we have described a few nested SQL queries and aggregate functions for information retrieval from a biological database.

1. To retrieve a limited number of records from a database.

>SELECT TOP number * FROM table_name;

For example, you have a table named sequences in your database having different columns such as OrgName, Sequence, OrgNumber, and so on. Since a single species may consist of multiple sequences and you want to retrieve only the top 3 sequences of that species, then use the following query.

>SELECT TOP 3 * FROM sequences;

In MySQL, the LIMIT clause does the same thing,

>SELECT column_names * FROM table_name

WHERE condition LIMIT number;

For example,

>SELECT * FROM sequences

WHERE OrgName=’Arabidopsis thaliana’

LIMIT 3;

2. To retrieve the minimum and maximum values from the selected column

To find the minimum value

>SELECT MIN(column_name) FROM table_name

WHERE condition;

For example,

>SELECT MIN(strains) AS MinimumStrains

FROM sequences;

To find the maximum value

>SELECT MAX(column_name) FROM table_name

WHERE condition;

For example,

>SELECT MAX(strains) AS MinimumStrains

FROM sequences

WHERE OrgName=’Arabidopsis thaliana’;

3. Aggregate functions in SQL

To find the number of rows matching a condition

>SELECT COUNT(column_name)

FROM table_name

WHERE condition;

For example,

>SELECT COUNT(OrgNumber)

FROM sequences;

To find the average value of a numeric column in a table

>SELECT AVG(column_name)

FROM table_name

WHERE condition;

For example,

>SELECT AVG(TotSeqs)

FROM sequences;

To find the sum of a numeric column

>SELECT SUM(column_name)

FROM table_name

WHERE condition;

For example,

>SELECT SUM(TotSeqs)

FROM sequences;

4. To search for a specific pattern in a column

>SELECT column1, column2, …

FROM table_name

WHERE column_name LIKE pattern;

There are several other wildcards that can be used with LIKE operator such as ‘*’, ‘[]’, ‘^’, etc. They can be used in combination as well. There are two wildcards mostly used with LIKE operator: ‘%’ and ‘_’.

% represents zero, one, or multiple characters, whereas, _ represents a single character. These wildcards are used before and/or after the specific characters or a pattern you are looking for.

For example, if you wish to search for sequences of a species using a few characters, then use the following query.

>SELECT Sequence, OrgName

FROM sequences

WHERE OrgName LIKE ‘%dopsis%’;

>SELECT Sequence, OrgName

FROM sequences

WHERE OrgName LIKE ‘%thaliana’;

>SELECT Sequence, OrgName

FROM sequences

WHERE OrgName LIKE ‘_ _abidop%’;

5. To specify multiple values in a WHERE clause

>SELECT column1, column2, …

FROM table_name

WHERE column_name IN (value1, value2, …);

For example,

>SELECT * FROM sequences

WHERE OrgName IN (‘Arabidopsis thaliana’, ‘Agrocybe aegerita’, ‘Homo sapiens’);

Or instead of entering values, SELECT statement can also be used as shown below,

>SELECT * FROM sequences

WHERE OrgName IN (SELECT statement);

If you wish to select records that are not present in the given values, then use the following query.

>SELECT * FROM sequences

WHERE OrgName NOT IN (‘Arabidopsis thaliana’, ‘Agrocybe aegerita’, ‘Homo sapiens’);

6. To select records within a given range in a table

>SELECT column1, column2, …

FROM table_name

WHERE column_name BETWEEN value1 AND value2;

For example,

>SELECT * FROM sequences

WHERE OrgNumber BETWEEN 102 AND 130;

Other SQL queries for complex information retrieval from a database will be described in upcoming articles.

Dr. Muniba is a Bioinformatician based in New Delhi, India. She has completed her PhD in Bioinformatics from South China University of Technology, Guangzhou, China. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

Advertisement
Click to comment

You must be logged in to post a comment Login

Leave a Reply

Bioinformatics Programming

sminalog_analysis.py – A new Python script to fetch top binding affinities from SMINA log file

Dr. Muniba Faiza

Published

on

sminalog_analysis.py – A new Python script to fetch top binding affinities from SMINA log file

In one of our previous posts, we provided a Python script for the virtual screening analysis of Autodock Vina. This script analyzes all log files obtained from docking of multiple ligands to a receptor and provides the binding affinities for top poses from each file. In this article, we are publishing a new Python script for the virtual screening analysis of SMINA [1]. (more…)

Continue Reading

Bioinformatics Programming

Installing Pycharm on Ubuntu (Linux)

Tariq Abdullah

Published

on

Installing pycharm on Ubuntu

Pycharm [1] is an integrated development environment (IDE) for developers. It combines Python developer tools and provides an easy graphical user interface. In this article, we are going to install Pycharm on Ubuntu. (more…)

Continue Reading

Algorithms

vs_Analysis.py: A Python Script to Analyze Virtual Screening Results of Autodock Vina

Dr. Muniba Faiza

Published

on

VS-Analysis: A Python Script to Analyze Virtual Screening Results of Autodock Vina

The output files obtained as a result of virtual screening (VS) using Autodock Vina may be large in number. It is difficult or quite impossible to analyze them manually. Therefore, we are providing a Python script to fetch top results (i.e., compounds showing low binding affinities). (more…)

Continue Reading

LATEST ISSUE

ADVERT