A generation ago, sequencing a single human genome required years of work and several hundred million dollars. Today, a mid-sized laboratory can do the same job in days. Yet according to Manan Chandra, a genomics specialist with roughly eleven years across the sequencing industry, many of those laboratories still struggle to turn that capability into reliable, scalable output.

The issue, he argues, is not the instruments. It is everything around them.

“In practice, many organisations struggle not because the technology itself is unavailable, but because implementation, workflow standardisation, and technical interpretation can become bottlenecks,” Chandra says.

A Career Spent At The Seam

Chandra holds a Master’s degree, with a thesis, in Functional Genomics from North Carolina State University. His work spans both Illumina short-read sequencing and Oxford Nanopore long-read sequencing, along with Sanger sequencing, qRT-PCR, and cloning. He has moved between laboratory operations, technical sales, and scientific consulting throughout his career, partnering with academic, biotechnology, and clinical organisations. That range gives him a perspective that specialists focused on a single platform or a single workflow stage tend to lack.

Most of his applied work has centred on the kinds of problems that rarely make headlines but routinely break sequencing projects. Sample-quality variation between batches. Library preparation failures that only surface during data analysis. Mismatched expectations between laboratory teams and the researchers commissioning the work. Normalisation processes that scale badly when sample volumes grow from dozens into the thousands.

He has worked on protocols for low-input and lower-quality DNA and RNA samples, the kind of material researchers cannot always avoid using. He has also helped develop dilution and quantification approaches that hold up under semi-automated processing. Small adjustments at those steps, he says, compound through every later stage of a sequencing run.

A Cited Record, Kept In The Background

Chandra has stayed active in scientific research throughout his career. He is a co-author on a PLOS ONE study that examined how Arabidopsis plants respond to pathogen infection at the transcript-isoform level rather than at the gene level alone. The paper has been cited roughly 150 times.

What sets his perspective apart is that he has worked across both the scientific and the commercial sides of genomics. Alongside hands-on laboratory work in next-generation sequencing, molecular biology, and workflow optimisation, he has worked in technical sales and scientific consulting, partnering with academic, biotechnology, and clinical organisations to match sequencing solutions to their research and operational needs. That range, he says, has given him a clearer view of how a new technology actually travels from a research laboratory into everyday use.

He treats the research as background, not as the centre of the story. The PLOS ONE work is something he points to when asked for evidence of how he thinks about sequencing data. The day-to-day work, the implementation problem, is what he wants the field to take more seriously.

Where The Field Is Heading

Two shifts inside genomics are reshaping the implementation question. The first is the steady move from short-read sequencing toward long-read platforms such as Oxford Nanopore, which Chandra describes as expanding what laboratories can attempt in genome assembly, structural variant analysis, and microbial genomics. The second is the integration of automation and machine-learning tools into sequencing workflows, particularly in quality control and downstream interpretation.

Neither, on its own, solves the implementation problem. Both, Chandra suggests, raise the premium on people who understand sequencing as a system rather than as a single instrument run.

For laboratories trying to scale, the question is not whether the technology will keep advancing. It will. The question is whether the practical work of running it well, the sample handling, the protocol design, the troubleshooting, the interpretation, can keep up. On that point, Chandra is direct: the labs that get this right will be the ones that treat operational expertise as a discipline in its own right, not as an afterthought to the science.