Abstract: Methods for analyzing DNA-containing samples are provided. The methods can comprise isolating a single genomic equivalent of DNA from the DNA-containing sample to provide a single isolated DNA molecule. The single isolated DNA molecule can be subjected to amplification conditions in the presence of one or more sets of unique molecularly tagged primers to provide one or more amplicons. Any spurious allelic sequences generated during the amplification process are tagged with an identical molecular tag. The methods can also include a step of determining the sequence of the one or more amplicons, in which the majority sequence for each code is selected as the sequence of the single original encapsulated target. The DNA-containing sample can be a forensic sample (e.g., mixed contributor sample), a fetal genetic screening sample, or a biological cell.

Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining sequence data for aptamers that bind to a target, where the sequence data has a first signal to noise ratio, generating, by a search process, a first set of aptamer sequences derived from the sequence data, obtaining subsequent sequence data for subsequent aptamers that bind to the target, where the subsequent aptamers includes aptamers synthesized from the first set of aptamer sequences, and the subsequent sequence data has a second signal to noise ratio greater than the first signal to noise ratio, generating, by a linear machine-learning model, a second set of aptamer sequences derived from the subsequent sequence data, and outputting the second set of aptamer sequences.

Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data for aptamers that bind to a target, measuring a first signal to noise ratio within the initial sequence data, provisioning, based on the first signal to noise ratio, a first machine-learning system, generating, by the first machine-learning system, a first set of aptamer sequences, obtaining subsequent sequence data for aptamers that bind to the target, measuring a second signal to noise ratio within the subsequent sequence data, provisioning, based on the second signal to noise ratio, a second machine-learning system, generating, by the second machine-learning system, a second set of aptamer sequences, and outputting the second set of aptamer sequences.