Methods are provided for genome editing. On example method includes editing a genome sequence of an organism with multiple edits simultaneously without precise knowledge of a phenotypic effect of each individual one of the multiple edits, wherein the multiple edits are selected based on a prediction of an aggregate phenotypic effect of the multiple edits on a phenotypic trait. The method also includes aggregating the multiple edits into multi-dimensional pools, whereby phenotypic effects of contrasting pools of edits are compared to ascertain which of the multiple edits are most likely to be causing large phenotypic effects while eliminating need to evaluate each edit separately. The organism may include one of: maize, soybean, wheat, sorghum, rice, cotton, rapeseed, sunflower, bean, tomato, squash, cucumber, melon, pepper, watermelon, eggplant, okra, pea, chickpea, lentil, peanut, onion, carrot, celery, beet, cauliflower, broccoli, cabbage, Brussels sprout, radish, black-eyed pea, potato, sweet-potato, sugar cane, cassava, and banana.

Panels of biomarkers, methods and systems are disclosed for determining gene expression, and diagnosing and treating melanoma.

Panels of biomarkers, methods and systems are disclosed for determining gene expression, and diagnosing and treating melanoma.

Disclosed herein are methods and systems of utilizing sequencing reads for detecting and quantifying the presence of a tissue type or a disease type in cell-free DNA prepared from blood samples.

Disclosed herein are methods and systems of utilizing sequencing reads for detecting and quantifying the presence of a tissue type or a disease type in cell-free DNA prepared from blood samples.

Disclosed herein are optimized guide RNAs (gRNAs) that have increased target binding specificity and reduced off-target binding. Further disclosed herein are methods of designing and using the optimized gRNAs.

Disclosed herein are optimized guide RNAs (gRNAs) that have increased target binding specificity and reduced off-target binding. Further disclosed herein are methods of designing and using the optimized gRNAs.

Systems and methods for spatial analysis of analytes are provided. A data structure is obtained comprising an image, as an array of pixel values, of a sample on a substrate having a identifier, fiducial markers and a set of capture spots. The pixel values are used to identify derived fiducial spots. The substrate identifier identifies a template having reference positions for reference fiducial spots and a corresponding coordinate system. The derived fiducial spots are aligned with the reference fiducial spots using an alignment algorithm to obtain a transformation between the derived and reference fiducial spots. The transformation and the template corresponding coordinate system are used to register the image to the set of capture spots. The registered image is then analyzed in conjunction with spatial analyte data associated with each capture spot, thereby performing spatial analysis of analytes.

Systems and methods for spatial analysis of analytes are provided. A data structure is obtained comprising an image, as an array of pixel values, of a sample on a substrate having a identifier, fiducial markers and a set of capture spots. The pixel values are used to identify derived fiducial spots. The substrate identifier identifies a template having reference positions for reference fiducial spots and a corresponding coordinate system. The derived fiducial spots are aligned with the reference fiducial spots using an alignment algorithm to obtain a transformation between the derived and reference fiducial spots. The transformation and the template corresponding coordinate system are used to register the image to the set of capture spots. The registered image is then analyzed in conjunction with spatial analyte data associated with each capture spot, thereby performing spatial analysis of analytes.

Factors affecting the fragmentation pattern of cell-free DNA (e.g., plasma DNA) and the applications, including those in molecular diagnostics, of the analysis of cell-free DNA fragmentation patterns are described. Various applications can use a property of a fragmentation pattern to determine a proportional contribution of a particular tissue type, to determine a genotype of a particular tissue type (e.g., fetal tissue in a maternal sample or tumor tissue in a sample from a cancer patient), and/or to identify preferred ending positions for a particular tissue type, which may then be used to determine a proportional contribution of a particular tissue type.