<｜begin▁of▁sentence｜>### Instruction:
Imagine we're mapping a specific genetic pathway in a gene therapy study, and we have modeled it as a path graph with 5 crucial genetic markers labeled from 1 to 5. We're using NetworkX to thoroughly analyze the connections between these markers, and we need to simulate how a genetic signal might propagate through this pathway from a certain marker.

We want to start from a particular genetic marker - let's say marker number 1 - and list out the sequence in which the signal would encounter each subsequent marker using a depth-first search (DFS) technique. This sequence should reflect a pre-order traversal starting from marker 1.

Additionally, in a separate experiment, it's necessary to understand how the signal propagation might be limited if it could only move through 2 connections due to some kind of genetic resistance or limitation. For this, we'd like to create a similar list to our initial DFS pre-order, but this time only exploring to a depth of 2 connections from marker 1.

This data representation could help us hypothesize how certain genetic traits or disorders are passed along the pathway and develop more targeted gene therapy interventions. Could you reframe our data extraction needs from NetworkX to maintain the scientific precision required for our genetic pathway analysis?
### Response:
import networkx as nx

# Create a path graph with 5 nodes
G = nx.path_graph(5)

# Generate nodes in DFS pre-order starting from node 0
pre_order_nodes = list(nx.dfs_preorder_nodes(G, source=0))

# Generate nodes in DFS pre-order with a depth limit of 2
pre_order_nodes_depth_limited = list(nx.dfs_preorder_nodes(G, source=0, depth_limit=2))

print('DFS pre-order nodes:', pre_order_nodes)
print('DFS pre-order nodes with depth limit of 2:', pre_order_nodes_depth_limited)
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