Mutational Analysis: Identifying SNVs on demand

[Alisa9]

Thanks for sheding light on such a interesting topic.
I also want to add some info. Before doing any data analysis, the company need to establish a proper data-driven culture.

What Is A Data-Driven Culture?
A data-driven culture is the practice of replacing gut feelings with decisions backed by data and facts, for example using advanced analytics models and qualitative data to forecast profit and revenue.
4 Components of A Data-driven Culture
Data maturity
Good data maturity is the basis for a data culture. It processes the raw material and its management. A company with solid data maturity has high-standard data quality, and there are available checks to sustain it.
For a high level of data maturity, it’s crucial to have metadata management in place and make sure it is aligned with the KPIs. Accordingly, you should record Data Lineage, which helps understand what occurred to it since its origin. Moreover, there should be a strong data governance structure, and employees have the proper level of access to data depending on their decision-making demands.
Other factors that impact data maturity are ease of access, usability, and scalable and agile infrastructure. For instance, if an organization has an archaic infrastructure, it will take too long to access data. In these cases, the company will not leverage data that is not simply accessible. Plus, organizations spend much time validating and creating alignment instead of the impact if there is no alignment of the KPIs.
Data-driven leadership
Leaders determine the culture of any company. To build a data-driven culture, leaders need to step up and lead by example. A data-driven leader raises the right questions and makes his/her team responsible, ensuring data is employed, and a structured process is followed. The leader considers data a strategic asset and makes “think and act data” a primary strategic priority.
For instance, a firm is going to adjust the default pricing for an app from annual to monthly subscriptions. The leader should guarantee that the teams are making decisions depending on data. The team will reach the decision according to an experiment – that with proper planning, the same size is satisfied. Furthermore, the experiment should indicate whether the uptick in the difference by adjusting the subscription plan is statistically crucial.
Data literacy
Data literacy means the ability to read, utilize, absorb, and interpret data toward consequential discussion and conclusion. For a company, data literacy does not mean employees deeply understand how to use and interpret data. It calls for everybody to get a specific level of data literacy based on their job role and the decisions they need to take.
Decision-making process
Data needs to become an essential part of the decision-making process to gain the best value.
Is there a planning mechanism to select between projects to work on or whether there is a lookback mechanism to review the decision?
For instance, If the marketing budget is distributed depending on the estimated return of investment, data might be employed to make decisions.
Full article I wrote here: What Is A Data-Driven Culture and How to Create One?

Alisa - Data analytics in business project

[thezgabby]

Hi everyone,

Really excited to be a part of this community. I am one of the team members of BioBox Analytics. We're a group of former academics that are building a powerful software platform for scientists working with NGS data that is intuitive and easy to use.

Our platform is a self-serve data analytics solution that integrates your NGS data with powerful analytics tools. We enable you to analyze and explore your data on demand and accelerate your time to scientific discovery.

Some features include:

• Create & run bioinformatic pipelines in the cloud (FASTQC, BWA, Mutect2, Delly)
• Auto-generate interactive visualizations like lollipop plots, heatmaps, volcano plots and scatter plots
• Connect data directly to public databases to derive relevant insights
• Leverage a gene search engine to provide in-depth information about your gene of interest and how it relates back to observations within your own datasets.

Right now, we support human and mouse RNAseq/WGS/WES data, with a focus on gene expression and mutational analysis.

Visit our website for more information.

Give it a try, it's free use. Let me know your thoughts - your feedback is how we improve. Appreciate it in advance. Cheers!