Turn Millions of Sequencing Reads into Clearer Insights.
Organize large sequencing datasets into structured sequence and frequency summaries to help researchers explore the diversity within their data.
Conceptual workflow. Illustrative data, not a real analysis.
What happens. Reads arrive as FASTA records, pasted text or sequence files, each with an identifier.
Why it matters. A real dataset can hold millions of reads, and many are copies of the same sequence. The number of reads is not the number of different sequences.
May produce. A set of records ready to be processed.
- 1
Large datasets
Sequencing datasets can contain millions of reads.
- 2
Clustered and scored
Identical sequences are clustered and each cluster is scored by its reads.
- 3
Traceable tables
Structured output and exports. Read identifiers are kept where supported.
Which service is this? NGS Data Analysis handles sequencing-derived sequences you already have. It is a translation, grouping and counting service, and does not analyse leads or design new ones.
How the workflow runs
Select a stage. Each shows what happens, what it uses and what it produces.
Input data
You supply sequencing-derived sequences, as pasted text, FASTA records or sequence files.
- Uses
- Sequence records with identifiers.
- Produces
- A set of records ready to be processed.
>read_000118 GAGGTGCAGCTGGTGGAGTCT…
From Inputs to Insights
- Sequencing-derived sequence files or tables
- Read or sample identifiers
- Optional grouping metadata
- Included: translation where the input supports it, grouping of identical sequences, absolute and relative frequencies, and exports.
- Not included: read-quality filtering, UMI or error correction, clonotype reconstruction, lineage inference or repertoire-level analysis.
- Frequency is abundance in your dataset. It does not show that a sequence binds or is functional.
Read scientific limitations
Counts describe the dataset you supply. A frequent sequence is not shown to be functional, and a rare one is not shown to be unimportant.
Sequencing and amplification can introduce errors and bias. This service does not correct for them.
Have sequencing data to make sense of?
Describe your dataset, its format and what you want to learn from it. We will discuss what the analysis can report.