Abstract: Disclosed herein are scientific instrument support systems, related methods, computing devices and computer-readable media. A method of mitigating distortion of an optical emission spectrum obtained from an optical emission spectrometer is provided. The method may comprise a step of obtaining a spectrum recorded with the spectrometer and a respective one or more condition parameters indicative of an operating condition at a time of recording the spectrum. The method may further comprise a step of providing a model configured to output, in response to the one or more condition parameters, one or more transform parameters of a transformation to be applied to the obtained spectrum. A transformation may be applied in accordance with the obtained one or more transform parameters to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and a baseline operating condition.

Abstract: A method for labeling sequence reads includes retrieving a sequence read having an associated flow measurement and an associated flow order; matching a sequence selected from a plurality of sequences with the sequence read, the sequence having a position within the sequence that has more than one acceptable variants; determining which variant of the more than one acceptable variants matches the sequence; generating a predicted flow measurement based on the matched sequence, the variant, and a flow order; and labeling the sequence read and associated flow measurement with the predicted flow measurement.

Abstract: A method for correcting signal measurements comprises an artificial neural network (ANN). The ANN receives a plurality of signal measurements in a channel of an input layer. The ANN is applied to the signal measurements and produces a plurality of signal correction values. The signal correction values may be subtracted from the signal measurements to form corrected signal measurements. The corrected signal measurements may be provided to a base caller to produce a sequence of base calls. The ANN may comprise a convolutional neural network (CNN). The CNN may have a U-NET architecture that includes an encoder and a decoder. The U-NET may include a Convolutional Block Attention Module (CBAM). The CBAM may applied to the outputs of a last pooling layer of the encoder and provides refined feature maps to a first layer of the decoder. The input signal measurements may be generated by a nucleic acid sequencing instrument.