Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Various embodiments disclosed relate to method for identification of preferred binding sites on intrinsically disorganized proteins (IDPs). The present disclosure includes methods including generating an IDP ensemble comprising one or more of the IDPs, sampling ligand interactions with the IDP ensemble to produce sampled ligand interactions, subjecting each of the sampled ligand interactions to an IDP ensemble docking, producing a differential binding score (DIBS) based on the sampled ligand interactions with the IDP ensemble docking, and modeling the DIBS to identify binding sites on the IDP ensemble.

Compositions, systems and methods for generating and using internal standard spike-in mixes including a combination of template spikes. Compositions, systems and methods described herein are directed to using the internal standard spike-in mixes to evaluate a set of workflow pipelines to perform differential abundance analyses on a sample containing variations of a target nucleic acid sequence of interest. Compositions, systems and methods described herein are directed to using the internal spike-in mixes to validate results obtained from differential abundance analyses performed on a sample containing variations of a target nucleic acid sequence of interest, where the variations may be of highly variable levels of relative abundance.

Compositions, systems and methods for generating and using internal standard spike-in mixes including a combination of template spikes. Compositions, systems and methods described herein are directed to using the internal standard spike-in mixes to evaluate a set of workflow pipelines to perform differential abundance analyses on a sample containing variations of a target nucleic acid sequence of interest. Compositions, systems and methods described herein are directed to using the internal spike-in mixes to validate results obtained from differential abundance analyses performed on a sample containing variations of a target nucleic acid sequence of interest, where the variations may be of highly variable levels of relative abundance.

The present disclosure provides a novel machine learning model capable of assisting those of ordinary skill in the art to conduct base editing by, inter alia, facilitating the selection of an appropriate guide RNA and base editor combination which are capable of conducting base editing at a certain level of efficiency and specificity on a given input target DNA sequence desired to be edited to produce an outcome genotype of interest. The disclosure also provides base editors (e.g., ABEs and CBEs), napDNAbps, cytidine deaminases, adenosine deaminases, nucleic acid sequences encoding base editors and components thereof, vectors, and cells. In addition, the disclosure provides methods of making biological or experimental training and/or validation data for training and/or validating the machine learning computational models, as well as, vectors, libraries, and nucleic acid sequences for use in obtaining said experimental training and/or validation data.

Processes and materials to detect cancer from a biopsy are described. In some cases, cell-free nucleic acids can be sequenced, and the sequencing result can be utilized to detect sequences derived from a neoplasm. Detection of somatic variants occurring in phase can indicate the presence of cancer in a diagnostic scan and a clinical intervention can be performed.

Processes and materials to detect cancer from a biopsy are described. In some cases, cell-free nucleic acids can be sequenced, and the sequencing result can be utilized to detect sequences derived from a neoplasm. Detection of somatic variants occurring in phase can indicate the presence of cancer in a diagnostic scan and a clinical intervention can be performed.

The disclosure belongs to the technical field of bioinformatics and bioengineering, and specifically, relates to a Levenshtein distance-based IRES screening method, a polynucleotide screened based on this method, a circular nucleic acid molecule including the polynucleotide, a cyclization precursor nucleic acid molecule, a recombinant nucleic acid molecule, a recombinant expression vector, a recombinant host cell, and use. In the disclosure, averages of Levenshtein distances between all sample sequences and to-be-predicted sequences are compared, to efficiently and accurately determine whether there is an IRES in the to-be-predicted sequence, which has advantages of high efficiency and an accurate screening result. In addition, the IRES screened by the IRES prediction method provided by the disclosure has high activity, thereby providing abundant translation initiation elements for application of the circular nucleic acid molecule in preparing a protein, serving as vaccines, producing a therapeutic protein, or serving as a means of gene therapy, etc.

The disclosure belongs to the technical field of bioinformatics and bioengineering, and specifically, relates to a Levenshtein distance-based IRES screening method, a polynucleotide screened based on this method, a circular nucleic acid molecule including the polynucleotide, a cyclization precursor nucleic acid molecule, a recombinant nucleic acid molecule, a recombinant expression vector, a recombinant host cell, and use. In the disclosure, averages of Levenshtein distances between all sample sequences and to-be-predicted sequences are compared, to efficiently and accurately determine whether there is an IRES in the to-be-predicted sequence, which has advantages of high efficiency and an accurate screening result. In addition, the IRES screened by the IRES prediction method provided by the disclosure has high activity, thereby providing abundant translation initiation elements for application of the circular nucleic acid molecule in preparing a protein, serving as vaccines, producing a therapeutic protein, or serving as a means of gene therapy, etc.