网上已经有很多博主给出了chatGPT在科研上如何使用，比如快速浏览一个领域的文献时，让chatGPT给总结文章的核心观点和结论，可以提高查阅文献的效率。再比如问一些开放性的问题，用chatGPT给出的答案来启发思路，协助创作。还有一个很多人关心的方面，代码编写能力。看到网上有人说chatGPT甚至可以替代部分程序员的工作，这也引起了小编的兴趣。今天我们就测试下让chatGPT写生物信息相关的代码会是什么样子。1、首先问了一个比较基础的，随便百度一下就有答案的问题。Q：pls help me write quick sort algorithm code.A：Certainly! Heres an implementation of the QuickSort algorithm in Python:defquick_sort(arr):if len(arr) <= 1:return arrelse: pivot = arr[0] less = [x for x in arr[1:] if x <= pivot] greater = [x for x in arr[1:] if x > pivot]return quick_sort(less) + [pivot] + quick_sort(greater)This implementation uses the first element of the array as the pivot. The less list contains all elements of the arr that are less than or equal to the pivot, and the greater list contains all elements that are greater than the pivot. Then, the function recursively sorts the less and greater lists, and concatenates the three lists to form the final sorted array.2、上面的答案正确，但是不够好，所以继续问这个问题。Q：Your code is not perfect. Because it requires more memory. Can you write a better answer?A：Yes, of course. I apologize for that mistake. Heres an improved implementation of QuickSort that sorts the array in-place, so it doesnt require additional memory:defquick_sort(arr, low, high):if low < high: pivot_index = partition(arr, low, high) quick_sort(arr, low, pivot_index – 1) quick_sort(arr, pivot_index + 1, high)defpartition(arr, low, high): pivot = arr[high] i = low – 1for j in range(low, high):if arr[j] <= pivot: i = i + 1 arr[i], arr[j] = arr[j], arr[i] arr[i + 1], arr[high] = arr[high], arr[i + 1]return i + 1In this implementation, the main QuickSort function takes three arguments: the array arr, the starting index low, and the ending index high. The partition function takes the same arguments, and it returns the index of the pivot after its been placed in its final position in the sorted array. The quick_sort function then calls itself recursively on the sub-arrays to the left and right of the pivot, until the sub-array has only one element.修改之后好多了，in-place可以减少内存使用。3、提取bam文件中的secondary比对Q：write a python script, which can extract secondary reads from bam fileA：Certainly! Heres a Python script that uses the pysam library to extract secondary reads from a BAM file:import pysambam_file = “path/to/bam_file.bam”output_file = “path/to/output_file.bam”with pysam.AlignmentFile(bam_file, “rb”) as input_bam, \ pysam.AlignmentFile(output_file, “wb”, template=input_bam) as output_bam:for read in input_bam.fetch():if read.is_secondary: output_bam.write(read)Heres a brief explanation of whats happening in the code:1、We first import the pysam library, which provides an interface for reading and writing BAM files.2、We specify the input and output file paths.3、We open the input BAM file in read mode and the output BAM file in write mode, using a with statement. We also pass the input BAM file as a template when opening the output BAM file, so that the header information is preserved.4、We loop through each read in the input BAM file using the fetch() method. This method returns an iterator over the reads in the BAM file.5、For each read, we check if it is a secondary read using the is_secondary attribute.6、If the read is a secondary read, we write it to the output BAM file using the write() method.7、Finally, we close both the input and output BAM files using the with statement.I hope this helps! Let me know if you have any questions or if theres anything else I can assist you with.4、上面提取的只有secondary比对，因此重新问。Q：please extract the paired reads of the secondary reads at the same timeA：Sure! Heres an updated version of the script that extracts paired reads for each secondary read in the input BAM file:import pysambam_file = “path/to/bam_file.bam”output_file = “path/to/output_file.bam”with pysam.AlignmentFile(bam_file, “rb”) as input_bam, \ pysam.AlignmentFile(output_file, “wb”, template=input_bam) as output_bam:forreadin input_bam.fetch():if read.is_secondary:if read.is_paired:if read.is_read1: pair = input_bam.mate(read)if pair.is_secondary: output_bam.write(read) output_bam.write(pair) elif read.is_read2: pair = input_bam.mate(read)if pair.is_secondary: output_bam.write(pair) output_bam.write(read)Heres whats happening in this updated version of the script:1、We start by importing the pysam library, as before.2、We specify the input and output file paths.3、We open the input BAM file in read mode and the output BAM file in write mode, using a with statement. We pass the input BAM file as a template when opening the output BAM file, so that the header information is preserved.4、We loop through each read in the input BAM file using the fetch() method, as before.5、For each read, we check if it is a secondary read using the is_secondary attribute.6、If the read is a secondary read, we check if it is paired using the is_paired attribute.7、If the read is paired, we check if it is the first or second read in the pair using the is_read1 and is_read2 attributes.8、If the read is the first read in the pair, we get the mate read using the mate() method of the input BAM file. We then check if the mate read is a secondary read using the is_secondary attribute.9、If both the original read and its mate are secondary reads, we write them to the output BAM file using the write() method of the output BAM file.10、If the read is the second read in the pair, we do the same thing, but we write the mate read before the original read in the output BAM file.11、Finally, we close both the input and output BAM files using the with statement.I hope this updated version of the script helps! Let me know if you have any questions or if theres anything else I can assist you with.写得复杂了点儿，但是和要求的不一样，我想让提取的是secondary read和它配对的read，它提取的是配对的两条都是secondary read的情况。不过格式和思路大致正确。5、提取pacbio测序的mRNA尾端polyaQ：write a python script, which can calculate mRNA polya length with PacBio ccsA：Sure! Heres an example Python script that uses the pysam library to calculate mRNA poly(A) tail lengths from PacBio CCS reads in a BAM file:import pysamdefget_polya_length(read):“”” Calculate the length of the poly(A) tail in a PacBio CCS read. “”” seq = read.query_sequence qual = read.query_qualities# Find the position of the first base with a quality score >= 30. start_pos = 0for i in range(len(qual)):if qual[i] >= 30: start_pos = ibreak# Find the position of the last base with a quality score >= 30. end_pos = len(seq) – 1for i in range(len(qual) – 1, -1, -1):if qual[i] >= 30: end_pos = ibreak# Count the number of As at the end of the read. poly_a_length = 0for i in range(end_pos, start_pos – 1, -1):if seq[i] == A: poly_a_length += 1else:breakreturn poly_a_length# Open the BAM file.bam_file = pysam.AlignmentFile(“my_reads.bam”, “rb”)# Loop over the reads in the BAM file and calculate the poly(A) tail length for each read.for read in bam_file:if read.is_unmapped or read.is_secondary:continue polya_length = get_polya_length(read) print(read.query_name, polya_length)# Close the BAM file.bam_file.close()Heres a brief explanation of whats happening in the code:1、We import the pysam library, which provides a Python interface for working with BAM files.2、We define a function called get_polya_length that takes a PacBio CCS read as input and calculates the length of the poly(A) tail in the read. We first find the position of the first and last base with a quality score of 30 or higher, then count the number of As at the end of the read.3、We open the BAM file using pysam.AlignmentFile.4、We loop over the reads in the BAM file and call get_polya_length on each read to calculate the poly(A) tail length. We skip any unmapped or secondary reads.5、We print the read name and poly(A) tail length to the console.6、We close the BAM file using bam_file.close().Note that this script assumes that the input BAM file contains PacBio CCS reads with poly(A) tails. If the reads are not PacBio CCS reads, or if they do not contain poly(A) tails, the results may not be meaningful.它默认测序read尾端是polyA，没有别的碱基了，所以先去除首尾低质量碱基，然后直接计算A的个数。大致正确，还写了挺多注释。6、ONT碱基识别Q：write a python script, which can make base calling from ONT sequence data with HMM modelA：Heres an example Python script that uses the pomegranate library to perform base calling from ONT sequence data using a hidden Markov model (HMM):import numpy as npimport osimport pomegranate as pm# Define the alphabet of basecalls.alphabet = “ACGT”# Define the transition matrix for the HMM.transition_matrix = np.array([ [0.85, 0.05, 0.05, 0.05], [0.05, 0.85, 0.05, 0.05], [0.05, 0.05, 0.85, 0.05], [0.05, 0.05, 0.05, 0.85]])# Define the emission probabilities for the HMM.emission_probabilities = np.array([ [0.98, 0.01, 0.01, 0.00], [0.01, 0.98, 0.00, 0.01], [0.00, 0.01, 0.98, 0.01], [0.01, 0.00, 0.01, 0.98]])# Define the HMM using the transition matrix and emission probabilities.hmm = pm.HiddenMarkovModel.from_matrix( transition_matrix, pm.DiscreteDistribution(dict(zip(alphabet, row))) for row in emission_probabilities)# Define a function to perform base calling on a sequence using the HMM.defbasecall(sequence):“”” Perform base calling on a sequence using the HMM. “””# Convert the sequence to a list of integers representing the basecalls. sequence_ints = [alphabet.index(base) for base in sequence]# Use the HMM to predict the most likely sequence of hidden states (i.e. basecalls). _, path = hmm.viterbi(sequence_ints)# Convert the sequence of hidden states to a string of basecalls. basecalls = .join([alphabet[state] for state in path])return basecalls# Load the ONT sequence data from a FASTQ file.with open(“my_data.fastq”, “r”) as f: lines = f.readlines()# Extract the sequence data from the FASTQ file.sequences = []for i in range(0, len(lines), 4): sequence = lines[i+1].strip() sequences.append(sequence)# Perform base calling on each sequence using the HMM.for sequence in sequences: basecalls = basecall(sequence) print(basecalls)Heres a brief explanation of whats happening in the code:1、We import the necessary libraries, including numpy and pomegranate.2、We define the alphabet of basecalls (“ACGT”).3、We define the transition matrix and emission probabilities for the HMM. In this example, we use a simple model where the transition probabilities are high within the same state (i.e. basecall) and low between different states, and the emission probabilities are high for the correct basecall and low for the other three basecalls.4、We define the HMM using pm.HiddenMarkovModel.from_matrix and the transition matrix and emission probabilities.5、We define a function called basecall that takes a sequence as input and performs base calling using the HMM. We first convert the sequence to a list of integers representing the basecalls, then use the HMM to predict the most likely sequence of hidden states (i.e. basecalls).大致思路应该是这样，它随便给了转换矩阵和发出概率矩阵，用了pomegranate这个包来构建模型，代码量非常少，思路可以借鉴。7、检测突变插入缺失Q：write a python script, which can detect mutation, insertion and deletion with PacBio sequence dataA：Heres an example Python script that uses the pysam and biopython libraries to detect mutations, insertions, and deletions in PacBio sequence data:import pysamfrom Bio import pairwise2# Open the BAM file using pysam.bamfile = pysam.AlignmentFile(“my_data.bam”, “rb”)# Iterate over each read in the BAM file.for read in bamfile:# Only consider reads with a primary alignment and no secondary or supplementary alignments.ifnot read.is_secondary andnot read.is_supplementary andnot read.is_unmapped:# Extract the reference sequence and aligned sequence from the read. ref_sequence = bamfile.get_reference_sequence(read.reference_id, read.reference_start, read.reference_end) aligned_sequence = read.query_alignment_sequence# Perform a pairwise alignment of the reference and aligned sequences using biopython. alignments = pairwise2.align.globalms(ref_sequence, aligned_sequence, 2, -1, -5, -1) best_alignment = alignments[0]# Extract the aligned sequences from the pairwise alignment. ref_aligned, aligned = best_alignment[0], best_alignment[1]# Iterate over each position in the aligned sequences.for i in range(len(ref_aligned)):# If the bases at this position do not match, its a mutation.if ref_aligned[i] != aligned[i]: print(f”Mutation at position {i+1} (reference base {ref_aligned[i]}, aligned base {aligned[i]})”)# If there is a gap in the aligned sequence but not in the reference sequence, its an insertion.elif aligned[i] == “-“and ref_aligned[i] != “-“: insertion_size = 1while i + insertion_size < len(ref_aligned) and aligned[i + insertion_size] == “-“: insertion_size += 1 print(f”Insertion of {insertion_size} bases at position {i+1}“)# If there is a gap in the reference sequence but not in the aligned sequence, its a deletion.elif aligned[i] != “-“and ref_aligned[i] == “-“: deletion_size = 1while i + deletion_size < len(ref_aligned) and ref_aligned[i + deletion_size] == “-“: deletion_size += 1 print(f”Deletion of {deletion_size} bases at position {i+1}“)# Close the BAM file.bamfile.close()Heres a brief explanation of whats happening in the code:1、We import the necessary libraries, including pysam and biopython.2、We open the BAM file using pysam.AlignmentFile.3、We iterate over each read in the BAM file and extract the reference sequence and aligned sequence.4、We perform a pairwise alignment of the reference and aligned sequences using pairwise2.align.globalms from biopython.5、We iterate over each position in the aligned sequences and check for mutations, insertions, and deletions.6、If the bases at a position do not match, we report a mutation.7、If there is a gap in the aligned sequence but not in the reference sequence, we report an insertion.8、If there is a gap in the reference sequence but not in the aligned sequence, we report a deletion.9、We close the BAM file using bamfile.close().调用biopython的序列比对模块，然后根据比对结果来给出突变插入缺失，比较直观简单8、pacbio的数据可以，那么ngs的呢Q：write a python script, which can detect mutation, insertion and deletion with NGS sequence dataA：Detecting mutations, insertions, and deletions (indels) in Next Generation Sequencing (NGS) data is a complex task that involves several steps. Heres a general outline of a Python script that could be used for this purpose:1、Read in the reference genome sequence and the NGS sequence data.2、Align the NGS reads to the reference genome using a suitable alignment tool (such as Bowtie, BWA, or STAR).3、Identify regions of the genome where there are differences between the reference and the aligned reads (called “variants”).4、Classify the variants based on their type (e.g. SNPs, insertions, deletions).5、Filter the variants to remove false positives (e.g. those that are present in low coverage regions or that have low quality scores).6、Annotate the variants with information about their functional impact (e.g. whether they are located in a gene, affect a protein-coding sequence, etc.).7、Output the final list of variants, along with their annotations, in a suitable format (such as VCF).Heres some sample code that demonstrates how to perform the first few steps of this process using the pysam library to read in BAM files and the pysamstats library to calculate read coverage and variant statistics:import pysamimport pysamstats# Read in the reference genome sequenceref = pysam.FastaFile(reference.fa)# Open the BAM file containing the NGS readsbam = pysam.AlignmentFile(reads.bam)# Get the list of contigs from the BAM file headercontigs = bam.references# Loop over each contig in the BAM filefor contig in contigs:# Get the coverage statistics for the contig coverage = pysamstats.load_coverage(bam, chrom=contig, start=0, end=ref.get_reference_length(contig))# Get the variant statistics for the contig variants = pysamstats.load_variation(bam, chrom=contig, start=0, end=ref.get_reference_length(contig))# Loop over each position in the contigfor i inrange(ref.get_reference_length(contig)):# Check if there is a variant at this positionif variants[i][variants] > 0:# Get the reference and alternate bases ref_base = ref.fetch(contig, i, i+1) alt_base = variants[i][alleles][1]# Determine the type of variant (SNP, insertion, or deletion)iflen(ref_base) == 1andlen(alt_base) == 1: variant_type = SNP elif len(ref_base) < len(alt_base): variant_type = insertionelse: variant_type = deletion# Print out the variant information print(contig, i+1, ref_base, alt_base, variant_type)This script reads in a reference genome sequence from a FASTA file and a set of aligned reads from a BAM file using the pysam library. It then loops over each contig in the BAM file and calculates the read coverage and variant statistics using the pysamstats library.For each position in the contig where there is a variant, the script determines the type of variant (SNP, insertion, or deletion) and prints out the position, the reference base, the alternate base, and the type of variant.Note that this script is just a starting point and would need to be modified and expanded to handle different types of mutations, filter out false positives, and annotate调用了pysamstats模块，来计算变异发生的情况。9、写个基础的比对算法Q：write a python script, which can make sequence global alignment, dont use modulesA：Heres an example Python script for global sequence alignment without using external modules:defglobal_alignment(seq1, seq2, match_score=1, mismatch_penalty=-1, gap_penalty=-1):# Initialize the alignment matrix. alignment_matrix = [[0for _ in range(len(seq2) + 1)] for _ in range(len(seq1) + 1)]# Initialize the first row and column of the alignment matrix.for i in range(len(seq1) + 1): alignment_matrix[i][0] = i * gap_penaltyfor j in range(len(seq2) + 1): alignment_matrix[0][j] = j * gap_penalty# Fill in the rest of the alignment matrix.for i in range(1, len(seq1) + 1):for j in range(1, len(seq2) + 1):# Calculate the match/mismatch score.if seq1[i-1] == seq2[j-1]: match_mismatch_score = match_scoreelse: match_mismatch_score = mismatch_penalty# Calculate the scores for the three possible moves. diagonal_score = alignment_matrix[i-1][j-1] + match_mismatch_score up_score = alignment_matrix[i-1][j] + gap_penalty left_score = alignment_matrix[i][j-1] + gap_penalty# Choose the move with the highest score. alignment_matrix[i][j] = max(diagonal_score, up_score, left_score)# Trace back through the alignment matrix to find the optimal alignment. aligned_seq1, aligned_seq2 = “”, “” i, j = len(seq1), len(seq2)while i > 0or j > 0:if i > 0and j > 0and seq1[i-1] == seq2[j-1]: aligned_seq1 = seq1[i-1] + aligned_seq1 aligned_seq2 = seq2[j-1] + aligned_seq2 i -= 1 j -= 1elif i > 0and alignment_matrix[i][j] == alignment_matrix[i-1][j] + gap_penalty: aligned_seq1 = seq1[i-1] + aligned_seq1 aligned_seq2 = “-“ + aligned_seq2 i -= 1else: aligned_seq1 = “-“ + aligned_seq1 aligned_seq2 = seq2[j-1] + aligned_seq2 j -= 1# Return the aligned sequences and alignment score.return aligned_seq1, aligned_seq2, alignment_matrix[-1][-1]Heres a brief explanation of whats happening in the code:1、We define a global_alignment function that takes two sequences and optional scoring parameters.2、We initialize the alignment matrix with zeros and fill in the first row and column with gap penalties.3、We fill in the rest of the alignment matrix using dynamic programming to calculate the scores for each position.4、We trace back through the alignment matrix to find the optimal alignment.5、We return the aligned sequences and alignment score.动态规划全局比对，大致看起来没什么毛病。10、画图Q：draw a plane in a 3D coordinate system with matplotlibA：To draw a plane in a 3D coordinate system with matplotlib, you can use the plot_surface function from the mplot3d toolkit. Heres an example script that plots a simple plane:import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D# Define the coordinates of the planex = np.array([-1, 1, 1, -1])y = np.array([-1, -1, 1, 1])z = np.array([0, 0, 0, 0])# Define the colors of the vertices of the planecolors = [r, g, b, y]# Create 2D arrays for the coordinatesX, Y = np.meshgrid(x, y)Z = np.meshgrid(z)[0]# Create a 3D plotfig = plt.figure()ax = fig.add_subplot(111, projection=3d)# Plot the planeax.plot_surface(X, Y, Z, color=colors)# Set the limits of the plotax.set_xlim([-2, 2])ax.set_ylim([-2, 2])ax.set_zlim([-2, 2])# Set the labels of the axesax.set_xlabel(X)ax.set_ylabel(Y)ax.set_zlabel(Z)# Show the plotplt.show()用的模块和思路没有问题，但是测试了一下有错误，于是我让它debug一下，结果代码如上，还是错的，Z应该是一个二维数组，代码中的是一维的，因此这里需要手动来debug了。总结起来，chatGPT的代码能力还是可以的，如果满分10分，小编给它打7分。首先，它的速度足够快，几秒钟就能给出结果，其次，代码质量也还凑活，注释很清晰，最后，对于复杂问题，比如突变检测问题，比如碱基识别，从头写代码会相当复杂，而且涉及到算法和参数测试，而chatGPT会走捷径，直接调用别人开发好的包来解决问题，在实际工作中，这也是有意义的，对于不需要深入研究的、以解决为导向的问题，可以用chatGPT帮助我们快速找到一种方法，一种思路，这不失为一种提高工作效率的方法。那么它是否能够替代程序员呢，我们看看chatGPT自己的回答：Q：How good chatGPT is at writing code and how it compares to the average programmerA：ChatGPT is a powerful language model that is capable of generating high-quality code in various programming languages. However, it is important to note that ChatGPT is not a substitute for a skilled and experienced human programmer.While ChatGPT can generate code that is syntactically correct and follows basic programming principles, it may not always produce the most efficient or optimal solutions. Additionally, ChatGPT may not be aware of domain-specific knowledge and best practices that a human programmer would have.On the other hand, ChatGPT has the advantage of being able to quickly generate code based on natural language instructions and examples, and can be a useful tool for prototyping, generating ideas, or automating certain repetitive programming tasks.Overall, ChatGPT is a powerful tool that can be used to assist human programmers in their work, but it should not be relied upon as a replacement for human expertise and experience.答案是不能，因为chatGPT虽然能够生成代码，但是不能保证是解决问题的最佳方案，它缺少的是人类在专业领域的知识和经验，但可以作为一个工具协助人们工作。
近来大火的语言模型chatGPT在语言处理能力上的进步让人震惊，和siri这类语音助手比较时，确实很有颠覆性。
 fill=%23FFFFFF%3E%3Crect x=249 y=126 width=1 height=1%3E%3C/rect%3E%3C/g%3E%3C/g%3E%3C/svg%3E)
